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Wampl S, Körner T, Meyerspeer M, Zaitsev M, Wolf M, Trattnig S, Wolzt M, Bogner W, Schmid AI. A modular motion compensation pipeline for prospective respiratory motion correction of multi-nuclear MR spectroscopy. Sci Rep 2024; 14:10781. [PMID: 38734781 PMCID: PMC11088657 DOI: 10.1038/s41598-024-61403-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 05/06/2024] [Indexed: 05/13/2024] Open
Abstract
Magnetic resonance (MR) acquisitions of the torso are frequently affected by respiratory motion with detrimental effects on signal quality. The motion of organs inside the body is typically decoupled from surface motion and is best captured using rapid MR imaging (MRI). We propose a pipeline for prospective motion correction of the target organ using MR image navigators providing absolute motion estimates in millimeters. Our method is designed to feature multi-nuclear interleaving for non-proton MR acquisitions and to tolerate local transmit coils with inhomogeneous field and sensitivity distributions. OpenCV object tracking was introduced for rapid estimation of in-plane displacements in 2D MR images. A full three-dimensional translation vector was derived by combining displacements from slices of multiple and arbitrary orientations. The pipeline was implemented on 3 T and 7 T MR scanners and tested in phantoms and volunteers. Fast motion handling was achieved with low-resolution 2D MR image navigators and direct implementation of OpenCV into the MR scanner's reconstruction pipeline. Motion-phantom measurements demonstrate high tracking precision and accuracy with minor processing latency. The feasibility of the pipeline for reliable in-vivo motion extraction was shown on heart and kidney data. Organ motion was manually assessed by independent operators to quantify tracking performance. Object tracking performed convincingly on 7774 navigator images from phantom scans and different organs in volunteers. In particular the kernelized correlation filter (KCF) achieved similar accuracy (74%) as scored from inter-operator comparison (82%) while processing at a rate of over 100 frames per second. We conclude that fast 2D MR navigator images and computer vision object tracking can be used for accurate and rapid prospective motion correction. This and the modular structure of the pipeline allows for the proposed method to be used in imaging of moving organs and in challenging applications like cardiac magnetic resonance spectroscopy (MRS) or magnetic resonance imaging (MRI) guided radiotherapy.
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Affiliation(s)
- Stefan Wampl
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Tito Körner
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Martin Meyerspeer
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Maxim Zaitsev
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Marcos Wolf
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Siegfried Trattnig
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Michael Wolzt
- Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Albrecht Ingo Schmid
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
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Motyka S, Weiser P, Bachrata B, Hingerl L, Strasser B, Hangel G, Niess E, Niess F, Zaitsev M, Robinson SD, Langs G, Trattnig S, Bogner W. Predicting dynamic, motion-related changes in B 0 field in the brain at a 7T MRI using a subject-specific fine-trained U-net. Magn Reson Med 2024; 91:2044-2056. [PMID: 38193276 DOI: 10.1002/mrm.29980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/28/2023] [Accepted: 11/30/2023] [Indexed: 01/10/2024]
Abstract
PURPOSE Subject movement during the MR examination is inevitable and causes not only image artifacts but also deteriorates the homogeneity of the main magnetic field (B0 ), which is a prerequisite for high quality data. Thus, characterization of changes to B0 , for example induced by patient movement, is important for MR applications that are prone to B0 inhomogeneities. METHODS We propose a deep learning based method to predict such changes within the brain from the change of the head position to facilitate retrospective or even real-time correction. A 3D U-net was trained on in vivo gradient-echo brain 7T MRI data. The input consisted of B0 maps and anatomical images at an initial position, and anatomical images at a different head position (obtained by applying a rigid-body transformation on the initial anatomical image). The output consisted of B0 maps at the new head positions. We further fine-trained the network weights to each subject by measuring a limited number of head positions of the given subject, and trained the U-net with these data. RESULTS Our approach was compared to established dynamic B0 field mapping via interleaved navigators, which suffer from limited spatial resolution and the need for undesirable sequence modifications. Qualitative and quantitative comparison showed similar performance between an interleaved navigator-equivalent method and proposed method. CONCLUSION It is feasible to predict B0 maps from rigid subject movement and, when combined with external tracking hardware, this information could be used to improve the quality of MR acquisitions without the use of navigators.
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Affiliation(s)
- Stanislav Motyka
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
| | - Paul Weiser
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Beata Bachrata
- Department of Medical Engineering, Carinthia University of Applied Sciences, Klagenfurt, Austria
| | - Lukas Hingerl
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Bernhard Strasser
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Gilbert Hangel
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Eva Niess
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
| | - Fabian Niess
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Maxim Zaitsev
- Department of Radiology - Medical Physics, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg - Medical Centre, Freiburg, Germany
| | - Simon Daniel Robinson
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Siegfried Trattnig
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
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Niess F, Strasser B, Hingerl L, Bader V, Frese S, Clarke WT, Duguid A, Niess E, Motyka S, Krššák M, Trattnig S, Scherer T, Lanzenberger R, Bogner W. Whole-brain deuterium metabolic imaging via concentric ring trajectory readout enables assessment of regional variations in neuronal glucose metabolism. Hum Brain Mapp 2024; 45:e26686. [PMID: 38647048 PMCID: PMC11034002 DOI: 10.1002/hbm.26686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 03/13/2024] [Accepted: 04/04/2024] [Indexed: 04/25/2024] Open
Abstract
Deuterium metabolic imaging (DMI) is an emerging magnetic resonance technique, for non-invasive mapping of human brain glucose metabolism following oral or intravenous administration of deuterium-labeled glucose. Regional differences in glucose metabolism can be observed in various brain pathologies, such as Alzheimer's disease, cancer, epilepsy or schizophrenia, but the achievable spatial resolution of conventional phase-encoded DMI methods is limited due to prolonged acquisition times rendering submilliliter isotropic spatial resolution for dynamic whole brain DMI not feasible. The purpose of this study was to implement non-Cartesian spatial-spectral sampling schemes for whole-brain 2H FID-MR Spectroscopic Imaging to assess time-resolved metabolic maps with sufficient spatial resolution to reliably detect metabolic differences between healthy gray and white matter regions. Results were compared with lower-resolution DMI maps, conventionally acquired within the same session. Six healthy volunteers (4 m/2 f) were scanned for ~90 min after administration of 0.8 g/kg oral [6,6']-2H glucose. Time-resolved whole brain 2H FID-DMI maps of glucose (Glc) and glutamate + glutamine (Glx) were acquired with 0.75 and 2 mL isotropic spatial resolution using density-weighted concentric ring trajectory (CRT) and conventional phase encoding (PE) readout, respectively, at 7 T. To minimize the effect of decreased signal-to-noise ratios associated with smaller voxels, low-rank denoising of the spatiotemporal data was performed during reconstruction. Sixty-three minutes after oral tracer uptake three-dimensional (3D) CRT-DMI maps featured 19% higher (p = .006) deuterium-labeled Glc concentrations in GM (1.98 ± 0.43 mM) compared with WM (1.66 ± 0.36 mM) dominated regions, across all volunteers. Similarly, 48% higher (p = .01) 2H-Glx concentrations were observed in GM (2.21 ± 0.44 mM) compared with WM (1.49 ± 0.20 mM). Low-resolution PE-DMI maps acquired 70 min after tracer uptake featured smaller regional differences between GM- and WM-dominated areas for 2H-Glc concentrations with 2.00 ± 0.35 mM and 1.71 ± 0.31 mM, respectively (+16%; p = .045), while no regional differences were observed for 2H-Glx concentrations. In this study, we successfully implemented 3D FID-MRSI with fast CRT encoding for dynamic whole-brain DMI at 7 T with 2.5-fold increased spatial resolution compared with conventional whole-brain phase encoded (PE) DMI to visualize regional metabolic differences. The faster metabolic activity represented by 48% higher Glx concentrations was observed in GM- compared with WM-dominated regions, which could not be reproduced using whole-brain DMI with the low spatial resolution protocol. Improved assessment of regional pathologic alterations using a fully non-invasive imaging method is of high clinical relevance and could push DMI one step toward clinical applications.
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Affiliation(s)
- Fabian Niess
- High Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Bernhard Strasser
- High Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Lukas Hingerl
- High Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Viola Bader
- High Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Sabina Frese
- High Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - William T. Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Anna Duguid
- High Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Eva Niess
- High Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
- Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
| | - Stanislav Motyka
- High Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
- Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
| | - Martin Krššák
- Department of Medicine III, Division of Endocrinology and MetabolismMedical University of ViennaViennaAustria
| | - Siegfried Trattnig
- High Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
- Institute for Clinical Molecular MRIKarl Landsteiner SocietySt. PöltenAustria
| | - Thomas Scherer
- Department of Medicine III, Division of Endocrinology and MetabolismMedical University of ViennaViennaAustria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH)Medical University of ViennaViennaAustria
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
- Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
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Hangel G, Kasprian G, Chambers S, Haider L, Lazen P, Koren J, Diehm R, Moser K, Tomschik M, Wais J, Winter F, Zeiser V, Gruber S, Aull-Watschinger S, Traub-Weidinger T, Baumgartner C, Feucht M, Dorfer C, Bogner W, Trattnig S, Pataraia E, Roessler K. Correction to: Implementation of a 7T Epilepsy Task Force consensus imaging protocol for routine presurgical epilepsy work-up: effect on diagnostic yield and lesion delineation. J Neurol 2024:10.1007/s00415-024-12257-9. [PMID: 38578499 DOI: 10.1007/s00415-024-12257-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Affiliation(s)
- Gilbert Hangel
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria.
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria.
- Medical Imaging Cluster, Medical University of Vienna, Vienna, Austria.
| | - Gregor Kasprian
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Stefanie Chambers
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
| | - Lukas Haider
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- NMR Research Unit, Faculty of Brain Science, Queens Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Radiology and Nuclear Medicine, VU University Medical Centre, Amsterdam, The Netherlands
| | - Philipp Lazen
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
| | - Johannes Koren
- Department of Neurology, Klinik Hietzing, Vienna, Austria
| | - Robert Diehm
- Center for Rare and Complex Childhood Onset Epilepsies, Member of ERN EpiCARE, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Katharina Moser
- Center for Rare and Complex Childhood Onset Epilepsies, Member of ERN EpiCARE, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Matthias Tomschik
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Jonathan Wais
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Fabian Winter
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Vitalij Zeiser
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Stephan Gruber
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria
| | | | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Martha Feucht
- Center for Rare and Complex Childhood Onset Epilepsies, Member of ERN EpiCARE, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Christian Dorfer
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Wolfgang Bogner
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria
| | - Siegfried Trattnig
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria
| | | | - Karl Roessler
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria
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Blömer S, Hingerl L, Marjańska M, Bogner W, Motyka S, Hangel G, Klauser A, Andronesi OC, Strasser B. Proton Free Induction Decay MRSI at 7T in the Human Brain Using an Egg-Shaped Modified Rosette K-Space Trajectory. medRxiv 2024:2024.03.26.24304840. [PMID: 38645249 PMCID: PMC11027556 DOI: 10.1101/2024.03.26.24304840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Purpose 1.1 Proton ( 1 H)-MRSI via spatial-spectral encoding poses high demands on gradient hardware at ultra-high fields and high-resolutions. Rosette trajectories help alleviate these problems, but at reduced SNR-efficiency due to their k-space densities not matching any desired k-space filter. We propose modified rosette trajectories, which more closely match a Hamming filter, and thereby improve SNR performance while still staying within gradient hardware limitations and without prolonging scan time. Methods 1.2Analytical and synthetic simulations were validated with phantom and in vivo measurements at 7 T. The rosette and modified rosette trajectories were measured in five healthy volunteers in six minutes in a 2D slice in the brain. A 3D sequence was measured in one volunteer within 19 minutes. The SNR, linewidth, CRLBs, lipid contamination and data quality of the proposed modified rosette trajectory were compared to the rosette trajectory. Results 1.3Using the modified rosette trajectories, an improved k-space weighting function was achieved resulting in an increase of up to 12% in SNR compared to rosette's dependent on the two additional trajectory parameters. Similar results were achieved for the theoretical SNR calculation based on k-space densities, as well as when using the pseudo-replica method for simulated, in-vivo and phantom data. The CRLBs improved slightly, but non-significantly for the modified rosette trajectories, while the linewidths and lipid contamination remained similar. Conclusion 1.4By improving the rosette trajectory's shape, modified rosette trajectories achieved higher SNR at the same scan time and data quality.
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Lazen P, Lima Cardoso P, Sharma S, Cadrien C, Roetzer-Pejrimovsky T, Furtner J, Strasser B, Hingerl L, Lipka A, Preusser M, Marik W, Bogner W, Widhalm G, Rössler K, Trattnig S, Hangel G. A Comparison of 7 Tesla MR Spectroscopic Imaging and 3 Tesla MR Fingerprinting for Tumor Localization in Glioma Patients. Cancers (Basel) 2024; 16:943. [PMID: 38473305 DOI: 10.3390/cancers16050943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/20/2024] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
This paper investigated the correlation between magnetic resonance spectroscopic imaging (MRSI) and magnetic resonance fingerprinting (MRF) in glioma patients by comparing neuro-oncological markers obtained from MRSI to T1/T2 maps from MRF. Data from 12 consenting patients with gliomas were analyzed by defining hotspots for T1, T2, and various metabolic ratios, and comparing them using Sørensen-Dice similarity coefficients (DSCs) and the distances between their centers of intensity (COIDs). The median DSCs between MRF and the tumor segmentation were 0.73 (T1) and 0.79 (T2). The DSCs between MRSI and MRF were the highest for Gln/tNAA (T1: 0.75, T2: 0.80, tumor: 0.78), followed by Gly/tNAA (T1: 0.57, T2: 0.62, tumor: 0.54) and tCho/tNAA (T1: 0.61, T2: 0.58, tumor: 0.45). The median values in the tumor hotspot were T1 = 1724 ms, T2 = 86 ms, Gln/tNAA = 0.61, Gly/tNAA = 0.28, Ins/tNAA = 1.15, and tCho/tNAA = 0.48, and, in the peritumoral region, were T1 = 1756 ms, T2 = 102 ms, Gln/tNAA = 0.38, Gly/tNAA = 0.20, Ins/tNAA = 1.06, and tCho/tNAA = 0.38, and, in the NAWM, were T1 = 950 ms, T2 = 43 ms, Gln/tNAA = 0.16, Gly/tNAA = 0.07, Ins/tNAA = 0.54, and tCho/tNAA = 0.20. The results of this study constitute the first comparison of 7T MRSI and 3T MRF, showing a good correspondence between these methods.
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Affiliation(s)
- Philipp Lazen
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Department for Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, 1090 Vienna, Austria
| | - Pedro Lima Cardoso
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Sukrit Sharma
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Cornelius Cadrien
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Department for Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
| | - Thomas Roetzer-Pejrimovsky
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
| | - Julia Furtner
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Danube Private University, 3500 Krems, Austria
| | - Bernhard Strasser
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Lukas Hingerl
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Alexandra Lipka
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Matthias Preusser
- Division of Oncology, Department of Internal Medicine I, Medical University of Vienna, 1090 Vienna, Austria
| | - Wolfgang Marik
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Wolfgang Bogner
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, 1090 Vienna, Austria
| | - Georg Widhalm
- Department for Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
| | - Karl Rössler
- Department for Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, 1090 Vienna, Austria
| | - Siegfried Trattnig
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, 1090 Vienna, Austria
- Institute for Clinical Molecular MRI, Karl Landsteiner Society, 3100 St. Pölten, Austria
| | - Gilbert Hangel
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Department for Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, 1090 Vienna, Austria
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7
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Hangel G, Kasprian G, Chambers S, Haider L, Lazen P, Koren J, Diehm R, Moser K, Tomschik M, Wais J, Winter F, Zeiser V, Gruber S, Aull-Watschinger S, Traub-Weidinger T, Baumgartner C, Feucht M, Dorfer C, Bogner W, Trattnig S, Pataraia E, Roessler K. Implementation of a 7T Epilepsy Task Force consensus imaging protocol for routine presurgical epilepsy work-up: effect on diagnostic yield and lesion delineation. J Neurol 2024; 271:804-818. [PMID: 37805665 PMCID: PMC10827812 DOI: 10.1007/s00415-023-11988-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/05/2023] [Indexed: 10/09/2023]
Abstract
OBJECTIVE Recently, the 7 Tesla (7 T) Epilepsy Task Force published recommendations for 7 T magnetic resonance imaging (MRI) in patients with pharmaco-resistant focal epilepsy in pre-surgical evaluation. The objective of this study was to implement and evaluate this consensus protocol with respect to both its practicability and its diagnostic value/potential lesion delineation surplus effect over 3 T MRI in the pre-surgical work-up of patients with pharmaco-resistant focal onset epilepsy. METHODS The 7 T MRI protocol consisted of T1-weighted, T2-weighted, high-resolution-coronal T2-weighted, fluid-suppressed, fluid-and-white-matter-suppressed, and susceptibility-weighted imaging, with an overall duration of 50 min. Two neuroradiologists independently evaluated the ability of lesion identification, the detection confidence for these identified lesions, and the lesion border delineation at 7 T compared to 3 T MRI. RESULTS Of 41 recruited patients > 12 years of age, 38 were successfully measured and analyzed. Mean detection confidence scores were non-significantly higher at 7 T (1.95 ± 0.84 out of 3 versus 1.64 ± 1.19 out of 3 at 3 T, p = 0.050). In 50% of epilepsy patients measured at 7 T, additional findings compared to 3 T MRI were observed. Furthermore, we found improved border delineation at 7 T in 88% of patients with 3 T-visible lesions. In 19% of 3 T MR-negative cases a new potential epileptogenic lesion was detected at 7 T. CONCLUSIONS The diagnostic yield was beneficial, but with 19% new 7 T over 3 T findings, not major. Our evaluation revealed epilepsy outcomes worse than ILAE Class 1 in two out of the four operated cases with new 7 T findings.
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Affiliation(s)
- Gilbert Hangel
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria.
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria.
- Medical Imaging Cluster, Medical University of Vienna, Vienna, Austria.
| | - Gregor Kasprian
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Stefanie Chambers
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
| | - Lukas Haider
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- NMR Research Unit, Faculty of Brain Science, Queens Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Radiology and Nuclear Medicine, VU University Medical Centre, Amsterdam, The Netherlands
| | - Philipp Lazen
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
| | - Johannes Koren
- Department of Neurology, Klinik Hietzing, Vienna, Austria
| | - Robert Diehm
- Center for Rare and Complex Childhood Onset Epilepsies, Member of ERN EpiCARE, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Katharina Moser
- Center for Rare and Complex Childhood Onset Epilepsies, Member of ERN EpiCARE, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Matthias Tomschik
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Jonathan Wais
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Fabian Winter
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Vitalij Zeiser
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Stephan Gruber
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria
| | | | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Martha Feucht
- Center for Rare and Complex Childhood Onset Epilepsies, Member of ERN EpiCARE, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Christian Dorfer
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Wolfgang Bogner
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria
| | - Siegfried Trattnig
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria
| | | | - Karl Roessler
- Department of Neurosurgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Vienna, Austria
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Klauser A, Strasser B, Bogner W, Hingerl L, Courvoisier S, Schirda C, Lazeyras F, Andronesi OC. ECCENTRIC: a fast and unrestrained approach for high-resolution in vivo metabolic imaging at ultra-high field MR. ArXiv 2023:arXiv:2305.13822v2. [PMID: 37292485 PMCID: PMC10246065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A novel method for fast and high-resolution metabolic imaging, called ECcentric Circle ENcoding TRajectorIes for Compressed sensing (ECCENTRIC), has been developed and implemented at 7 Tesla MRI. ECCENTRIC is a non-Cartesian spatial-spectral encoding method optimized to accelerate magnetic resonance spectroscopic imaging (MRSI) with high signal-to-noise at ultra-high field. The approach provides flexible and random ( k , t ) sampling without temporal interleaving to improve spatial response function and spectral quality. ECCENTRIC needs low gradient amplitudes and slew-rates that reduces electrical, mechanical and thermal stress of the scanner hardware, and is robust to timing imperfection and eddy-current delays. Combined with a model-based low-rank reconstruction, this approach enables simultaneous imaging of up to 14 metabolites over the whole-brain at 2-3mm isotropic resolution in 4-10 minutes. In healthy volunteers ECCENTRIC demonstrated unprecedented spatial mapping of fine structural details of human brain neurochemistry. This innovative tool introduces a novel approach to neuroscience, providing new insights into the exploration of brain activity and physiology.
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Affiliation(s)
- Antoine Klauser
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Switzerland
| | - Bernhard Strasser
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston
- High-Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High-Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lukas Hingerl
- High-Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Sebastien Courvoisier
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Switzerland
| | - Claudiu Schirda
- Department of Radiology, University of Pittsburgh School of Medicine,Pittsburgh, Pennsylvania, USA
| | - Francois Lazeyras
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Switzerland
| | - Ovidiu C. Andronesi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston
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Lipka A, Bogner W, Dal-Bianco A, Hangel GJ, Rommer PS, Strasser B, Motyka S, Hingerl L, Berger T, Leutmezer F, Gruber S, Trattnig S, Niess E. Metabolic Insights into Iron Deposition in Relapsing-Remitting Multiple Sclerosis via 7 T Magnetic Resonance Spectroscopic Imaging. Neuroimage Clin 2023; 40:103524. [PMID: 37839194 PMCID: PMC10590870 DOI: 10.1016/j.nicl.2023.103524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/26/2023] [Accepted: 10/07/2023] [Indexed: 10/17/2023]
Abstract
OBJECTIVE To investigate the metabolic pattern of different types of iron accumulation in multiple sclerosis (MS) lesions, and compare metabolic alterations within and at the periphery of lesions and newly emerging lesions in vivo according to iron deposition. METHODS 7 T MR spectroscopic imaging and susceptibility-weighted imaging was performed in 31 patients with relapsing-remitting MS (16 female/15 male; mean age, 36.9 ± 10.3 years). Mean metabolic ratios of four neuro-metabolites were calculated for regions of interest (ROI) of normal appearing white matter (NAWM), "non-iron" (lesion without iron accumulation on SWI), and three distinct types of iron-laden lesions ("rim": distinct rim-shaped iron accumulation; "area": iron deposition across the entire lesions; "transition": transition between "area" and "rim" accumulation shape), and for lesion layers of "non-iron" and "rim" lesions. Furthermore, newly emerging "non-iron" and "iron" lesions were compared longitudinally, as measured before their appearance and one year later. RESULTS Thirty-nine of 75 iron-containing lesions showed no distinct paramagnetic rim. Of these, "area" lesions exhibited a 65% higher mIns/tNAA (p = 0.035) than "rim" lesions. Comparing lesion layers of both "non-iron" and "rim" lesions, a steeper metabolic gradient of mIns/tNAA ("non-iron" +15%, "rim" +40%) and tNAA/tCr ("non-iron" -15%, "rim" -35%) was found in "iron" lesions, with the lesion core showing +22% higher mIns/tNAA (p = 0.005) and -23% lower tNAA/tCr (p = 0.048) in "iron" compared to "non-iron" lesions. In newly emerging lesions, 18 of 39 showed iron accumulation, with the drop in tNAA/tCr after lesion formation remaining significantly lower compared to pre-lesional tissue over time in "iron" lesions (year 0: p = 0.013, year 1: p = 0.041) as opposed to "non-iron" lesions (year 0: p = 0.022, year 1: p = 0.231). CONCLUSION 7 T MRSI allows in vivo characterization of different iron accumulation types each presenting with a distinct metabolic profile. Furthermore, the larger extent of neuronal damage in lesions with a distinct iron rim was reconfirmed via reduced tNAA/tCr concentrations, but with metabolic differences in lesion development between (non)-iron-containing lesions. This highlights the ability of MRSI to further investigate different types of iron accumulation and suggests possible implications for disease monitoring.
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Affiliation(s)
- Alexandra Lipka
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna.
| | | | - Gilbert J Hangel
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Paulus S Rommer
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Bernhard Strasser
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Stanislav Motyka
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lukas Hingerl
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Thomas Berger
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Fritz Leutmezer
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Stephan Gruber
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Siegfried Trattnig
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria
| | - Eva Niess
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna
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10
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Niess F, Strasser B, Hingerl L, Niess E, Motyka S, Hangel G, Krššák M, Gruber S, Spurny-Dworak B, Trattnig S, Scherer T, Lanzenberger R, Bogner W. Reproducibility of 3D MRSI for imaging human brain glucose metabolism using direct ( 2H) and indirect ( 1H) detection of deuterium labeled compounds at 7T and clinical 3T. Neuroimage 2023; 277:120250. [PMID: 37414233 PMCID: PMC11019874 DOI: 10.1016/j.neuroimage.2023.120250] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/25/2023] [Accepted: 06/23/2023] [Indexed: 07/08/2023] Open
Abstract
INTRODUCTION Deuterium metabolic imaging (DMI) and quantitative exchange label turnover (QELT) are novel MR spectroscopy techniques for non-invasive imaging of human brain glucose and neurotransmitter metabolism with high clinical potential. Following oral or intravenous administration of non-ionizing [6,6'-2H2]-glucose, its uptake and synthesis of downstream metabolites can be mapped via direct or indirect detection of deuterium resonances using 2H MRSI (DMI) and 1H MRSI (QELT), respectively. The purpose of this study was to compare the dynamics of spatially resolved brain glucose metabolism, i.e., estimated concentration enrichment of deuterium labeled Glx (glutamate+glutamine) and Glc (glucose) acquired repeatedly in the same cohort of subjects using DMI at 7T and QELT at clinical 3T. METHODS Five volunteers (4 m/1f) were scanned in repeated sessions for 60 min after overnight fasting and 0.8 g/kg oral [6,6'-2H2]-glucose administration using time-resolved 3D 2H FID-MRSI with elliptical phase encoding at 7T and 3D 1H FID-MRSI with a non-Cartesian concentric ring trajectory readout at clinical 3T. RESULTS One hour after oral tracer administration regionally averaged deuterium labeled Glx4 concentrations and the dynamics were not significantly different over all participants between 7T 2H DMI and 3T 1H QELT data for GM (1.29±0.15 vs. 1.38±0.26 mM, p=0.65 & 21±3 vs. 26±3 µM/min, p=0.22) and WM (1.10±0.13 vs. 0.91±0.24 mM, p=0.34 & 19±2 vs. 17±3 µM/min, p=0.48). Also, the observed time constants of dynamic Glc6 data in GM (24±14 vs. 19±7 min, p=0.65) and WM (28±19 vs. 18±9 min, p=0.43) dominated regions showed no significant differences. Between individual 2H and 1H data points a weak to moderate negative correlation was observed for Glx4 concentrations in GM (r=-0.52, p<0.001), and WM (r=-0.3, p<0.001) dominated regions, while a strong negative correlation was observed for Glc6 data GM (r=-0.61, p<0.001) and WM (r=-0.70, p<0.001). CONCLUSION This study demonstrates that indirect detection of deuterium labeled compounds using 1H QELT MRSI at widely available clinical 3T without additional hardware is able to reproduce absolute concentration estimates of downstream glucose metabolites and the dynamics of glucose uptake compared to 2H DMI data acquired at 7T. This suggests significant potential for widespread application in clinical settings especially in environments with limited access to ultra-high field scanners and dedicated RF hardware.
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Affiliation(s)
- Fabian Niess
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Lazarettgasse 14, Vienna A-1090, Austria.
| | - Bernhard Strasser
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Lazarettgasse 14, Vienna A-1090, Austria
| | - Lukas Hingerl
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Lazarettgasse 14, Vienna A-1090, Austria
| | - Eva Niess
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Lazarettgasse 14, Vienna A-1090, Austria; Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Austria
| | - Stanislav Motyka
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Lazarettgasse 14, Vienna A-1090, Austria; Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Austria
| | - Gilbert Hangel
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Lazarettgasse 14, Vienna A-1090, Austria; Department of Neurosurgery, Medical University of Vienna, Austria
| | - Martin Krššák
- Department of Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, Austria
| | - Stephan Gruber
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Lazarettgasse 14, Vienna A-1090, Austria; Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Austria
| | - Benjamin Spurny-Dworak
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Siegfried Trattnig
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Lazarettgasse 14, Vienna A-1090, Austria; Institute for Clinical Molecular MRI, Karl Landsteiner Society, Pölten 3100St, Austria
| | - Thomas Scherer
- Department of Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Lazarettgasse 14, Vienna A-1090, Austria; Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Austria
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11
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Bednarik P, Goranovic D, Svatkova A, Niess F, Hingerl L, Strasser B, Deelchand DK, Spurny-Dworak B, Krssak M, Trattnig S, Hangel G, Scherer T, Lanzenberger R, Bogner W. 1H magnetic resonance spectroscopic imaging of deuterated glucose and of neurotransmitter metabolism at 7 T in the human brain. Nat Biomed Eng 2023; 7:1001-1013. [PMID: 37106154 PMCID: PMC10861140 DOI: 10.1038/s41551-023-01035-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 03/30/2023] [Indexed: 04/29/2023]
Abstract
Impaired glucose metabolism in the brain has been linked to several neurological disorders. Positron emission tomography and carbon-13 magnetic resonance spectroscopic imaging (MRSI) can be used to quantify the metabolism of glucose, but these methods involve exposure to radiation, cannot quantify downstream metabolism, or have poor spatial resolution. Deuterium MRSI (2H-MRSI) is a non-invasive and safe alternative for the quantification of the metabolism of 2H-labelled substrates such as glucose and their downstream metabolic products, yet it can only measure a limited number of deuterated compounds and requires specialized hardware. Here we show that proton MRSI (1H-MRSI) at 7 T has higher sensitivity, chemical specificity and spatiotemporal resolution than 2H-MRSI. We used 1H-MRSI in five volunteers to differentiate glutamate, glutamine, γ-aminobutyric acid and glucose deuterated at specific molecular positions, and to simultaneously map deuterated and non-deuterated metabolites. 1H-MRSI, which is amenable to clinically available magnetic-resonance hardware, may facilitate the study of glucose metabolism in the brain and its potential roles in neurological disorders.
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Affiliation(s)
- Petr Bednarik
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark.
- Department of Radiology, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark.
| | - Dario Goranovic
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Alena Svatkova
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
- Department of Radiology, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
- Department of Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria
| | - Fabian Niess
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lukas Hingerl
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Bernhard Strasser
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Dinesh K Deelchand
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Benjamin Spurny-Dworak
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Martin Krssak
- Department of Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria
| | - Siegfried Trattnig
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Gilbert Hangel
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Thomas Scherer
- Department of Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
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12
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Niess F, Hingerl L, Strasser B, Bednarik P, Goranovic D, Niess E, Hangel G, Krššák M, Spurny-Dworak B, Scherer T, Lanzenberger R, Bogner W. Noninvasive 3-Dimensional 1 H-Magnetic Resonance Spectroscopic Imaging of Human Brain Glucose and Neurotransmitter Metabolism Using Deuterium Labeling at 3T : Feasibility and Interscanner Reproducibility. Invest Radiol 2023; 58:431-437. [PMID: 36735486 PMCID: PMC10184811 DOI: 10.1097/rli.0000000000000953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 12/15/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVES Noninvasive, affordable, and reliable mapping of brain glucose metabolism is of critical interest for clinical research and routine application as metabolic impairment is linked to numerous pathologies, for example, cancer, dementia, and depression. A novel approach to map glucose metabolism noninvasively in the human brain has been presented recently on ultrahigh-field magnetic resonance (MR) scanners (≥7T) using indirect detection of deuterium-labeled glucose and downstream metabolites such as glutamate, glutamine, and lactate. The aim of this study was to demonstrate the feasibility to noninvasively detect deuterium-labeled downstream glucose metabolites indirectly in the human brain via 3-dimensional (3D) proton ( 1 H) MR spectroscopic imaging on a clinical 3T MR scanner without additional hardware. MATERIALS AND METHODS This prospective, institutional review board-approved study was performed in 7 healthy volunteers (mean age, 31 ± 4 years, 5 men/2 women) after obtaining written informed consent. After overnight fasting and oral deuterium-labeled glucose administration, 3D metabolic maps were acquired every ∼4 minutes with ∼0.24 mL isotropic spatial resolution using real-time motion-, shim-, and frequency-corrected echo-less 3D 1 H-MR spectroscopic Imaging on a clinical routine 3T MR system. To test the interscanner reproducibility of the method, subjects were remeasured on a similar 3T MR system. Time courses were analyzed using linear regression and nonparametric statistical tests. Deuterium-labeled glucose and downstream metabolites were detected indirectly via their respective signal decrease in dynamic 1 H MR spectra due to exchange of labeled and unlabeled molecules. RESULTS Sixty-five minutes after deuterium-labeled glucose administration, glutamate + glutamine (Glx) signal intensities decreased in gray/white matter (GM/WM) by -1.63 ± 0.3/-1.0 ± 0.3 mM (-13% ± 3%, P = 0.02/-11% ± 3%, P = 0.02), respectively. A moderate to strong negative correlation between Glx and time was observed in GM/WM ( r = -0.64, P < 0.001/ r = -0.54, P < 0.001), with 60% ± 18% ( P = 0.02) steeper slopes in GM versus WM, indicating faster metabolic activity. Other nonlabeled metabolites showed no significant changes. Excellent intrasubject repeatability was observed across scanners for static results at the beginning of the measurement (coefficient of variation 4% ± 4%), whereas differences were observed in individual Glx dynamics, presumably owing to physiological variation of glucose metabolism. CONCLUSION Our approach translates deuterium metabolic imaging to widely available clinical routine MR scanners without specialized hardware, offering a safe, affordable, and versatile (other substances than glucose can be labeled) approach for noninvasive imaging of glucose and neurotransmitter metabolism in the human brain.
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Affiliation(s)
- Fabian Niess
- From the High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lukas Hingerl
- From the High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Bernhard Strasser
- From the High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Petr Bednarik
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, University Hospital Amager and Hvidovre, Hvidovre, Denmark
- Department of Radiology, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark
| | - Dario Goranovic
- From the High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Eva Niess
- From the High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Gilbert Hangel
- From the High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Neurosurgery
| | - Martin Krššák
- Department of Medicine III, Division of Endocrinology and Metabolism
| | - Benjamin Spurny-Dworak
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Thomas Scherer
- Department of Medicine III, Division of Endocrinology and Metabolism
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- From the High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
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13
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Marik W, Cardoso PL, Springer E, Bogner W, Preusser M, Widhalm G, Hangel G, Hainfellner JA, Rausch I, Weber M, Schmidbauer V, Traub-Weidinger T, Trattnig S. Evaluation of Gliomas with Magnetic Resonance Fingerprinting with PET Correlation-A Comparative Study. Cancers (Basel) 2023; 15:2740. [PMID: 37345077 DOI: 10.3390/cancers15102740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/08/2023] [Accepted: 05/10/2023] [Indexed: 06/23/2023] Open
Abstract
OBJECTIVES Advanced MR imaging of brain tumors is still mainly based on qualitative imaging. PET imaging offers additive metabolic information, and MR fingerprinting (MRF) offers a novel approach to quantitative data acquisition. The purpose of this study was to evaluate the ability of MRF to predict tumor regions and grading in combination with PET. METHODS Seventeen patients with histologically verified infiltrating gliomas and available amino-acid PET data were enrolled. ROIs for solid tumor parts (SPo), perifocal edema (ED1), and normal-appearing white matter (NAWM) were selected on conventional MRI sequences and aligned to the MRF and PET images. The predictability of gliomas by region and grading as well as intermodal correlations were assessed. RESULTS For MRF, we calculated an overall predictability by region (SPo, ED1, and NAWM) for all of the MRF parameters of 76.5%, 47.1%, and 94.1%, respectively. The overall ability to distinguish low- from high-grade gliomas using MRF was 88.9% for LGG and 75% for HGG, with an accuracy of 82.4%, a ppV of 85.71%, and an npV of 80%. PET positivity was found in 13/17 patients for solid tumor parts, and in 3/17 patients for the edema region. However, there was no significant difference in region-specific MRF values between PET positive and PET negative patients. CONCLUSIONS MRF and PET provide quantitative measurements of the tumor tissue characteristics of gliomas, with good predictability. Nonetheless, the results are dissimilar, reflecting the different underlying mechanisms of each method.
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Affiliation(s)
- Wolfgang Marik
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Pedro Lima Cardoso
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Elisabeth Springer
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Institute of Radiology, Hietzing Hospital, 1130 Vienna, Austria
| | - Wolfgang Bogner
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Matthias Preusser
- Division of Oncology, Department of Internal Medicine I, Medical University of Vienna, 1090 Vienna, Austria
| | - Georg Widhalm
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
| | - Gilbert Hangel
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
| | - Johannes A Hainfellner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
| | - Ivo Rausch
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Michael Weber
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Victor Schmidbauer
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Siegfried Trattnig
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
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14
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Niess F, Strasser B, Hingerl L, Niess E, Motyka S, Hangel G, Krššák M, Gruber S, Spurny-Dworak B, Trattnig S, Scherer T, Lanzenberger R, Bogner W. Reproducibility of 3D MRSI for imaging human brain glucose metabolism using direct ( 2 H) and indirect ( 1 H) detection of deuterium labeled compounds at 7T and clinical 3T. medRxiv 2023:2023.04.17.23288672. [PMID: 37131634 PMCID: PMC10153308 DOI: 10.1101/2023.04.17.23288672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Introduction Deuterium metabolic imaging (DMI) and quantitative exchange label turnover (QELT) are novel MR spectroscopy techniques for non-invasive imaging of human brain glucose and neurotransmitter metabolism with high clinical potential. Following oral or intravenous administration of non-ionizing [6,6'- 2 H 2 ]-glucose, its uptake and synthesis of downstream metabolites can be mapped via direct or indirect detection of deuterium resonances using 2 H MRSI (DMI) and 1 H MRSI (QELT), respectively. The purpose of this study was to compare the dynamics of spatially resolved brain glucose metabolism, i.e., estimated concentration enrichment of deuterium labeled Glx (glutamate+glutamine) and Glc (glucose) acquired repeatedly in the same cohort of subjects using DMI at 7T and QELT at clinical 3T. Methods Five volunteers (4m/1f) were scanned in repeated sessions for 60 min after overnight fasting and 0.8g/kg oral [6,6'- 2 H 2 ]-glucose administration using time-resolved 3D 2 H FID-MRSI with elliptical phase encoding at 7T and 3D 1 H FID-MRSI with a non-Cartesian concentric ring trajectory readout at clinical 3T. Results One hour after oral tracer administration regionally averaged deuterium labeled Glx 4 concentrations and the dynamics were not significantly different over all participants between 7T 2 H DMI and 3T 1 H QELT data for GM (1.29±0.15 vs. 1.38±0.26 mM, p=0.65 & 21±3 vs. 26±3 µM/min, p=0.22) and WM (1.10±0.13 vs. 0.91±0.24 mM, p=0.34 & 19±2 vs. 17±3 µM/min, p=0.48). Also, the observed time constants of dynamic Glc 6 data in GM (24±14 vs. 19±7 min, p=0.65) and WM (28±19 vs. 18±9 min, p=0.43) dominated regions showed no significant differences. Between individual 2 H and 1 H data points a weak to moderate negative correlation was observed for Glx 4 concentrations in GM (r=-0.52, p<0.001), and WM (r=-0.3, p<0.001) dominated regions, while a strong negative correlation was observed for Glc 6 data GM (r=- 0.61, p<0.001) and WM (r=-0.70, p<0.001). Conclusion This study demonstrates that indirect detection of deuterium labeled compounds using 1 H QELT MRSI at widely available clinical 3T without additional hardware is able to reproduce absolute concentration estimates of downstream glucose metabolites and the dynamics of glucose uptake compared to 2 H DMI data acquired at 7T. This suggests significant potential for widespread application in clinical settings especially in environments with limited access to ultra-high field scanners and dedicated RF hardware.
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15
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Spurny-Dworak B, Reed MB, Handschuh P, Vanicek T, Spies M, Bogner W, Lanzenberger R. The influence of season on glutamate and GABA levels in the healthy human brain investigated by magnetic resonance spectroscopy imaging. Hum Brain Mapp 2023; 44:2654-2663. [PMID: 36840505 PMCID: PMC10028653 DOI: 10.1002/hbm.26236] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/22/2023] [Accepted: 02/02/2023] [Indexed: 02/26/2023] Open
Abstract
Seasonal changes in neurotransmitter systems have been demonstrated in imaging studies and are especially noticeable in diseased states such as seasonal affective disorder (SAD). These modulatory neurotransmitters, such as serotonin, are influencing glutamatergic and GABAergic neurotransmission. Furthermore, central components of the circadian pacemaker are regulated by GABA (the suprachiasmatic nucleus) or glutamate (e.g., the retinohypothalamic tract). Therefore, we explored seasonal differences in the GABAergic and glutamatergic system in 159 healthy individuals using magnetic resonance spectroscopy imaging with a GABA-edited 3D-MEGA-LASER sequence at 3T. We quantified GABA+/tCr, GABA+/Glx, and Glx/tCr ratios (GABA+, GABA+ macromolecules; Glx, glutamate + glutamine; tCr, total creatine) in five different subcortical brain regions. Differences between time periods throughout the year, seasonal patterns, and stationarity were tested using ANCOVA models, curve fitting approaches, and unit root and stationarity tests, respectively. Finally, Spearman correlation analyses between neurotransmitter ratios within each brain region and cumulated daylight and global radiation were performed. No seasonal or monthly differences, seasonal patterns, nor significant correlations could be shown in any region or ratio. Unit root and stationarity tests showed stable patterns of GABA+/tCr, GABA+/Glx, and Glx/tCr levels throughout the year, except for hippocampal Glx/tCr. Our results indicate that neurotransmitter levels of glutamate and GABA in healthy individuals are stable throughout the year. Hence, despite the important correction for age and gender in the analyses of MRS derived GABA and glutamate, a correction for seasonality in future studies does not seem necessary. Future investigations in SAD and other psychiatric patients will be of high interest.
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Affiliation(s)
- B Spurny-Dworak
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - M B Reed
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - P Handschuh
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - T Vanicek
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - M Spies
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - W Bogner
- Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
| | - R Lanzenberger
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
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16
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Clarke WT, Hingerl L, Strasser B, Bogner W, Valkovič L, Rodgers CT. Three-dimensional, 2.5-minute, 7T phosphorus magnetic resonance spectroscopic imaging of the human heart using concentric rings. NMR Biomed 2023; 36:e4813. [PMID: 35995750 PMCID: PMC7613900 DOI: 10.1002/nbm.4813] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 07/27/2022] [Accepted: 08/10/2022] [Indexed: 05/06/2023]
Abstract
A three-dimensional (3D), density-weighted, concentric rings trajectory (CRT) magnetic resonance spectroscopic imaging (MRSI) sequence is implemented for cardiac phosphorus (31 P)-MRS at 7 T. The point-by-point k-space sampling of traditional phase-encoded chemical shift imaging (CSI) sequences severely restricts the minimum scan time at higher spatial resolutions. Our proposed CRT sequence implements a stack of concentric rings, with a variable number of rings and planes spaced to optimise the density of k-space weighting. This creates flexibility in acquisition time, allowing acquisitions substantially faster than traditional phase-encoded CSI sequences, while retaining high signal-to-noise ratio (SNR). We first characterise the SNR and point-spread function of the CRT sequence in phantoms. We then evaluate it at five different acquisition times and spatial resolutions in the hearts of five healthy participants at 7 T. These different sequence durations are compared with existing published 3D acquisition-weighted CSI sequences with matched acquisition times and spatial resolutions. To minimise the effect of noise on the short acquisitions, low-rank denoising of the spatiotemporal data was also performed after acquisition. The proposed sequence measures 3D localised phosphocreatine to adenosine triphosphate (PCr/ATP) ratios of the human myocardium in 2.5 min, 2.6 times faster than the minimum scan time for acquisition-weighted phase-encoded CSI. Alternatively, in the same scan time, a 1.7-times smaller nominal voxel volume can be achieved. Low-rank denoising reduced the variance of measured PCr/ATP ratios by 11% across all protocols. The faster acquisitions permitted by 7-T CRT 31 P-MRSI could make cardiac stress protocols or creatine kinase rate measurements (which involve repeated scans) more tolerable for patients without sacrificing spatial resolution.
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Affiliation(s)
- William T. Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Lukas Hingerl
- High‐field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
| | - Bernhard Strasser
- High‐field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
| | - Wolfgang Bogner
- High‐field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
| | - Ladislav Valkovič
- Oxford Centre for Clinical Magnetic Resonance Research, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- Department of Imaging Methods, Institute of Measurement ScienceSlovak Academy of SciencesBratislavaSlovakia
| | - Christopher T. Rodgers
- Oxford Centre for Clinical Magnetic Resonance Research, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- Wolfson Brain Imaging Centre, Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
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17
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Hangel G, Rausch I, Furtner J, Roetzer-Pejrimovsky T, Preusser M, Bogner W, Rössler K, Trattnig S, Traub-Weidinger T, Widhalm G. JS07.4.A Correspondence of glutamine and glycine imaging based on 7T MRSI to amino acid PET. Neuro Oncol 2022. [DOI: 10.1093/neuonc/noac174.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
New approaches for 7 Tesla magnetic resonance spectroscopic imaging (MRSI) allow the simultaneous imaging of multiple neuro-oncological biomarkers with 3-4 mm resolution in clinically feasible measurement times. Specifically, the amino acids glutamine (Gln) and Glycine (Gly), were previously limited to single voxel detection at lower fields. Both could add to our capabilities to resolve heterogeneous tumour metabolism.
Purpose
To progress the validation of Gln and Gly as neuro-oncological markers by conducting the first comparison to amino acid PET in a cohort of glioma patients.
Material and Methods
In 24 glioma patients (WHO 2021 classification), we quantitatively compared 7T MRSI (3D, 3.4 mm isotropic resolution, 15 min scan time) and routine PET (FET or MET). Within manual tumour segmentations, we defined hotspot volumes of interest (VOI) for the ratios of total choline (tCho, clinical standard reference), Gln, Gly to total N-acetylaspartate (tNAA) and PET tumour-to-brain ratios (TBR), all with a cut-off threshold of 1.6. For these VOIs, we calculated VOI volumes and median ratios as well as Dice similarity coefficients (DSC) and centre of intensity distance (CoI), between MRSI and PET ratios.
Results
We found that Gln and Gly ratios to tNAA had a higher correspondence to PET-based amino acid metabolism than tCho. Our resulting median VOI volumes were 19.08±23.10 cm³ for tCho/tNAA, 33.68±24.60 cm³ for Gln/tNAA, and 22.38±18.49 cm³ for Gly/tNAA compared to 24.33±30.46 cm³ for PET, with correlation coefficients >0.5 for all MRSI hotspot values in relation to PET volumes. Median ratios were 0.52±0.13 for tCho/tNAA, 0.61±0.25 for Gln/tNAA, 0.33±0.15 for Gly/tNAA and 2.11±0.42 for PET. The median DSCs to PET amounted to 0.53±0.36 for tCho/tNAA, 0.66±0.40 for Gln/tNAA, and 0.57±0.36 for Gly/tNAA, while the median CoI distances were 0.56±0.43 cm for tCho/tNAA, 0.39±0.22 cm for Gln/tNAA, and 0.45±0.48 cm for Gly/tNAA.
Conclusion
With this first study that compared high-resolution 3D-MRSI at 7 Tesla to amino acid PET and a quantitative evaluation, we demonstrated that Gln and Gly corresponded better to PET than tCho, which is the main marker used in clinical MR, both within the study and compared to previous literature. Future research is needed to clearly define the benefits of 7T MRSI for neuro-oncology such as the identification of tumour microenvironments or non-invasive determination of molecular-pathologic markers. Gln could be further explored by the application of Gln-based PET tracers to MR-PET. We still see further developments of MRSI methods, such as motion correction or absolute quantification of concentrations instead of ratios, as necessary to obtain such goals.
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Affiliation(s)
- G Hangel
- Medical University of Vienna , Vienna , Austria
| | - I Rausch
- Medical University of Vienna , Vienna , Austria
| | - J Furtner
- Medical University of Vienna , Vienna , Austria
| | | | - M Preusser
- Medical University of Vienna , Vienna , Austria
| | - W Bogner
- Medical University of Vienna , Vienna , Austria
| | - K Rössler
- Medical University of Vienna , Vienna , Austria
| | - S Trattnig
- Medical University of Vienna , Vienna , Austria
- Institute for Clinical Molecular MRI, Karl Landsteiner Society , St. Pölten , Austria
| | | | - G Widhalm
- Medical University of Vienna , Vienna , Austria
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18
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Hangel G, Sharma S, Cadrien C, Furtner J, Furtner J, Roetzer-Pejrimovsky T, Preusser M, Bogner W, Rössler K, Trattnig S, Widhalm G. P15.03.A The value of 7 Tesla MR spectroscopic imaging for improved preoperative determination of the tumor grade and IDH status in gliomas: preliminary data. Neuro Oncol 2022. [DOI: 10.1093/neuonc/noac174.293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Introduction
A new generation of MR spectroscopic imaging (MRSI) methods using 7T scanners have demonstrated the capability to resolve more neuro- and oncometabolites at higher resolutions than clinical routine MRSI. In a cohort of glioma patients, we explored the automated preoperative and noninvasive classification of IDH-mutation status and tumor grade based on 7T MRSI.
Methods
This retrospective study included 36 patients (15 female) with histologically confirmed diffusely infiltrating glioma WHO grade 2-4 (9 grade 2, 9 grade 3 and 18 grade 4) and known IDH status (21 IDH1-mut, 15 IDH-wt) with an available 7T MRSI scan of sufficient data quality. The 3D MRSI scan had a 3.4 mm isotropic resolution and 15 minutes acquisition time. 12 spectral components were classified voxel-wise, including choline, glutamine and glycine. Within a tumor segmentation based on routine 3T imaging, we used a random forest algorithm for the voxel-wise classification of IDH mutation and grade (into low or high grade). Training used the leave-one-out cross validation method (i.e., for every patient data set, the other 35 datasets were used as training set) and feature selection out of the available combinations for metabolite ratios (e.g., glutamine to choline). The resulting voxel classifications were aggregated into a mean probability per patient that was the base for receiver-operator characteristic (ROC) curves both for grade and IDH status.
Results
The classification algorithm obtained an area under the curve (AUC) for IDH determination of 0.85 (e.g., 75% sensitivity and 95% specificity). For grade determination, the AUC was 0.88 (e.g., 87% sensitivity and 89% specificity). In comparison, the AUC per voxel would have resulted in an AUC of 0.66 for both. Further, classification by individual metabolite ratios resulted in lower AUCs in all cases.
Conclusions
According to our preliminary data, preoperative 7T MRSI is capable to determine the correct glioma grade and IDH status with high sensitivity and specificity by leveraging the extended metabolic panel width and voxel amount. By increasing this cohort in future, we intend to confirm our initial results and we also plan to extend classification to more molecular-pathological features (e.g., TERT). Thus, even a voxel-wise classification of tumor microenvironments could be attempted. Further improvements in 7T MRSI methodology such as absolute instead of relative quantification would also aid these attempts.
In summary, 7T MRSI has shown its potential for improved preoperative characterization of gliomas.
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Affiliation(s)
- G Hangel
- Medical University of Vienna , Vienna , Austria
| | - S Sharma
- Medical University of Vienna , Vienna , Austria
| | - C Cadrien
- Medical University of Vienna , Vienna , Austria
| | - J Furtner
- Medical University of Vienna , Vienna , Austria
| | - J Furtner
- Medical University of Vienna , Vienna , Austria
| | | | - M Preusser
- Medical University of Vienna , Vienna , Austria
| | - W Bogner
- Medical University of Vienna , Vienna , Austria
| | - K Rössler
- Medical University of Vienna , Vienna , Austria
| | - S Trattnig
- Medical University of Vienna , Vienna , Austria
- Institute for Clinical Molecular MRI, Karl Landsteiner Society , St. Pölten , Austria
| | - G Widhalm
- Medical University of Vienna , Vienna , Austria
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19
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Spurny-Dworak B, Godbersen GM, Reed MB, Unterholzner J, Vanicek T, Baldinger-Melich P, Hahn A, Kranz GS, Bogner W, Lanzenberger R, Kasper S. The Impact of Theta-Burst Stimulation on Cortical GABA and Glutamate in Treatment-Resistant Depression: A Surface-Based MRSI Analysis Approach. Front Mol Neurosci 2022; 15:913274. [PMID: 35909445 PMCID: PMC9328022 DOI: 10.3389/fnmol.2022.913274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Theta burst stimulation (TBS) belongs to one of the biological antidepressant treatment options. When applied bilaterally, excitatory intermittent TBS (iTBS) is commonly targeted to the left and inhibitory continuous TBS (cTBS) to the right dorsolateral prefrontal cortex. TBS was shown to influence neurotransmitter systems, while iTBS is thought to interfere with glutamatergic circuits and cTBS to mediate GABAergic neurotransmission. Objectives: We aimed to expand insights into the therapeutic effects of TBS on the GABAergic and glutamatergic system utilizing 3D-multivoxel magnetic resonance spectroscopy imaging (MRSI) in combination with a novel surface-based MRSI analysis approach to investigate changes of cortical neurotransmitter levels in patients with treatment-resistant depression (TRD). Methods: Twelve TRD patients (five females, mean age ± SD = 35 ± 11 years) completed paired MRSI measurements, using a GABA-edited 3D-multivoxel MEGA-LASER sequence, before and after 3 weeks of bilateral TBS treatment. Changes in cortical distributions of GABA+/tNAA (GABA+macromolecules relative to total N-acetylaspartate) and Glx/tNAA (Glx = mixed signal of glutamate and glutamine), were investigated in a surface-based region-of-interest (ROI) analysis approach. Results: ANCOVAs revealed a significant increase in Glx/tNAA ratios in the left caudal middle frontal area (pcorr. = 0.046, F = 13.292), an area targeted by iTBS treatment. Whereas, contralateral treatment with cTBS evoked no alterations in glutamate or GABA concentrations. Conclusion: This study demonstrates surface-based adaptions in the stimulation area to the glutamate metabolism after excitatory iTBS but not after cTBS, using a novel surface-based analysis of 3D-MRSI data. The reported impact of facilitatory iTBS on glutamatergic neurotransmission provides further insight into the neurobiological effects of TBS in TRD.
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Affiliation(s)
- Benjamin Spurny-Dworak
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | | | - Murray Bruce Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Jakob Unterholzner
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Thomas Vanicek
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Pia Baldinger-Melich
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Georg S. Kranz
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Wolfgang Bogner
- Department of Biomedical Imaging and Image-guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- *Correspondence: Rupert Lanzenberger Siegfried Kasper
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Department of Molecular Neuroscience, Center for Brain Research, Medical University of Vienna, Vienna, Austria
- *Correspondence: Rupert Lanzenberger Siegfried Kasper
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20
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Spurny-Dworak B, Handschuh P, Spies M, Kaufmann U, Seiger R, Klöbl M, Konadu ME, Reed MB, Ritter V, Baldinger-Melich P, Bogner W, Kranz GS, Lanzenberger R. Effects of sex hormones on brain GABA and glutamate levels in a cis- and transgender cohort. Psychoneuroendocrinology 2022; 138:105683. [PMID: 35176535 DOI: 10.1016/j.psyneuen.2022.105683] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/14/2022] [Accepted: 01/27/2022] [Indexed: 01/23/2023]
Abstract
Sex hormones affect the GABAergic and glutamatergic neurotransmitter system as demonstrated in animal studies. However, human research has mostly been correlational in nature. Here, we aimed at substantiating causal interpretations of the interaction between sex hormones and neurotransmitter function by using magnetic resonance spectroscopy imaging (MRSI) to study the effect of gender-affirming hormone treatment (GHT) in transgender individuals. Fifteen trans men (TM) with a DSM-5 diagnosis of gender dysphoria, undergoing GHT, and 15 age-matched cisgender women (CW), receiving no therapy, underwent MRSI before and after at least 12 weeks. Additionally, sex differences in neurotransmitter levels were evaluated in an independent sample of 80 cisgender men and 79 cisgender women. Mean GABA+ (combination of GABA and macromolecules) and Glx (combination of glutamate and glutamine) ratios to total creatine (GABA+/tCr, Glx/tCr) were calculated in five predefined regions-of-interest (hippocampus, insula, pallidum, putamen and thalamus). Linear mixed models analysis revealed a significant measurement by gender identity effect (pcorr. = 0.048) for GABA+/tCr ratios in the hippocampus, with the TM cohort showing decreased GABA+/tCr levels after GHT compared to CW. Moreover, analysis of covariance showed a significant sex difference in insula GABA+/tCr ratios (pcorr. = 0.049), indicating elevated GABA levels in cisgender women compared to cisgender men. Our study demonstrates GHT treatment-induced GABA+/tCr reductions in the hippocampus, indicating hormone receptor activation on GABAergic cells and testosterone-induced neuroplastic processes within the hippocampus. Moreover, elevated GABA levels in the female compared to the male insula highlight the importance of including sex as factor in future MRS studies. DATA AVAILABILITY STATEMENT: Due to data protection laws processed data is available from the authors upon reasonable request. Please contact rupert.lanzenberger@meduniwien.ac.at with any questions or requests.
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Affiliation(s)
- B Spurny-Dworak
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - P Handschuh
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - M Spies
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - U Kaufmann
- Department of Obstetrics and Gynecology, Medical University of Vienna, Austria
| | - R Seiger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - M Klöbl
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - M E Konadu
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - M B Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - V Ritter
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - P Baldinger-Melich
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - W Bogner
- Department of Biomedical Imaging and Image-guided Therapy, High Field MR Centre, Medical University of Vienna, Austria
| | - G S Kranz
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
| | - R Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria.
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21
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Hangel G, Niess E, Lazen P, Bednarik P, Bogner W, Strasser B. Emerging methods and applications of ultra-high field MR spectroscopic imaging in the human brain. Anal Biochem 2022; 638:114479. [PMID: 34838516 DOI: 10.1016/j.ab.2021.114479] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 10/15/2021] [Accepted: 11/16/2021] [Indexed: 12/21/2022]
Abstract
Magnetic Resonance Spectroscopic Imaging (MRSI) of the brain enables insights into the metabolic changes and fluxes in diseases such as tumors, multiple sclerosis, epilepsy, or hepatic encephalopathy, as well as insights into general brain functionality. However, the routine application of MRSI is mostly hampered by very low signal-to-noise ratios (SNR) due to the low concentrations of metabolites, about 10000 times lower than water. Furthermore, MRSI spectra have a dense information content with many overlapping metabolite resonances, especially for proton MRSI. MRI scanners at ultra-high field strengths, like 7 T or above, offer the opportunity to increase SNR, as well as the separation between resonances, thus promising to solve both challenges. Yet, MRSI at ultra-high field strengths is challenged by decreased B0- and B1-homogeneity, shorter T2 relaxation times, stronger chemical shift displacement errors, and aggravated lipid contamination. Therefore, to capitalize on the advantages of ultra-high field strengths, these challenges must be overcome. This review focuses on the challenges MRSI of the human brain faces at ultra-high field strength, as well as the possible applications to this date.
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Affiliation(s)
- Gilbert Hangel
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria; Department of Neurosurgery, Medical University of Vienna, Austria
| | - Eva Niess
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Philipp Lazen
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Petr Bednarik
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Bernhard Strasser
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria.
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22
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Springer E, Cardoso PL, Strasser B, Bogner W, Preusser M, Widhalm G, Nittka M, Koerzdoerfer G, Szomolanyi P, Hangel G, Hainfellner JA, Marik W, Trattnig S. MR Fingerprinting-A Radiogenomic Marker for Diffuse Gliomas. Cancers (Basel) 2022; 14:cancers14030723. [PMID: 35158990 PMCID: PMC8833555 DOI: 10.3390/cancers14030723] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/22/2022] [Accepted: 01/28/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: Advanced MR imaging (MRI) of brain tumors is mainly based on qualitative contrast images. MR Fingerprinting (MRF) offers a novel approach. The purpose of this study was to use MRF-derived T1 and T2 relaxation maps to differentiate diffuse gliomas according to isocitrate dehydrogenase (IDH) mutation. (2) Methods: Twenty-four patients with histologically verified diffuse gliomas (14 IDH-mutant, four 1p/19q-codeleted, 10 IDH-wildtype) were enrolled. MRF T1 and T2 relaxation times were compared to apparent diffusion coefficient (ADC), relative cerebral blood volume (rCBV) within solid tumor, peritumoral edema, and normal-appearing white matter (NAWM), using contrast-enhanced MRI, diffusion-, perfusion-, and susceptibility-weighted imaging. For perfusion imaging, a T2* weighted perfusion sequence with leakage correction was used. Correlations of MRF T1 and T2 times with two established conventional sequences for T1 and T2 mapping were assessed (a fast double inversion recovery-based MR sequence ('MP2RAGE') for T1 quantification and a multi-contrast spin echo-based sequence for T2 quantification). (3) Results: MRF T1 and T2 relaxation times were significantly higher in the IDH-mutant than in IDH-wildtype gliomas within the solid part of the tumor (p = 0.024 for MRF T1, p = 0.041 for MRF T2). MRF T1 and T2 relaxation times were significantly higher in the IDH-wildtype than in IDH-mutant gliomas within peritumoral edema less than or equal to 1cm adjacent to the tumor (p = 0.038 for MRF T1 mean, p = 0.010 for MRF T2 mean). In the solid part of the tumor, there was a high correlation between MRF and conventionally measured T1 and T2 values (r = 0.913, p < 0.001 for T1, r = 0.775, p < 0.001 for T2), as well as between MRF and ADC values (r = 0.813, p < 0.001 for T2, r = 0.697, p < 0.001 for T1). The correlation was weak between the MRF and rCBV values (r = -0.374, p = 0.005 for T2, r = -0.181, p = 0.181 for T1). (4) Conclusions: MRF enables fast, single-sequence based, multi-parametric, quantitative tissue characterization of diffuse gliomas and may have the potential to differentiate IDH-mutant from IDH-wildtype gliomas.
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Affiliation(s)
- Elisabeth Springer
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Institute of Radiology, Hietzing Hospital, 1130 Vienna, Austria
| | - Pedro Lima Cardoso
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
| | - Bernhard Strasser
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
| | - Wolfgang Bogner
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Correspondence: ; Tel.: +431-40-400-64710
| | - Matthias Preusser
- Division of Oncology, Department of Internal Medicine I, Medical University of Vienna, 1090 Vienna, Austria;
| | - Georg Widhalm
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria;
| | - Mathias Nittka
- Siemens Healthineers, 91052 Erlangen, Germany; (M.N.); (G.K.)
| | | | - Pavol Szomolanyi
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Department of Imaging Methods, Institute of Measurement Science, Slovak Academy of Sciences, 84104 Bratislava, Slovakia
| | - Gilbert Hangel
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria;
| | - Johannes A. Hainfellner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria;
| | - Wolfgang Marik
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria;
| | - Siegfried Trattnig
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Christian Doppler Laboratory for Clinical Molecular MR Imaging, Medical University of Vienna, 1090 Vienna, Austria
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23
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Heckova E, Dal-Bianco A, Strasser B, Hangel GJ, Lipka A, Motyka S, Hingerl L, Rommer PS, Berger T, Hnilicová P, Kantorová E, Leutmezer F, Kurča E, Gruber S, Trattnig S, Bogner W. Extensive Brain Pathologic Alterations Detected with 7.0-T MR Spectroscopic Imaging Associated with Disability in Multiple Sclerosis. Radiology 2022; 303:141-150. [PMID: 34981978 DOI: 10.1148/radiol.210614] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Background MR spectroscopic imaging (MRSI) allows in vivo assessment of brain metabolism and is of special interest in multiple sclerosis (MS), where morphologic MRI cannot depict major parts of disease activity. Purpose To evaluate the ability of 7.0-T MRSI to depict and visualize pathologic alterations in the normal-appearing white matter (NAWM) and cortical gray matter (CGM) in participants with MS and to investigate their relation to disability. Materials and Methods Free-induction decay MRSI was performed at 7.0 T. Participants with MS and age- and sex-matched healthy controls were recruited prospectively between January 2016 and December 2017. Metabolic ratios were obtained in white matter lesions, NAWM, and CGM regions. Subgroup analysis for MS-related disability based on Expanded Disability Status Scale (EDSS) scores was performed using analysis of covariance. Partial correlations were applied to explore associations between metabolic ratios and disability. Results Sixty-five participants with MS (mean age ± standard deviation, 34 years ± 9; 34 women) and 20 age- and sex-matched healthy controls (mean age, 32 years ± 7; 11 women) were evaluated. Higher signal intensity of myo-inositol (mI) with and without reduced signal intensity of N-acetylaspartate (NAA) was visible on metabolic images in the NAWM of participants with MS. A higher ratio of mI to total creatine (tCr) was observed in the NAWM of the centrum semiovale of all MS subgroups, including participants without disability (marginal mean ± standard error, healthy controls: 0.78 ± 0.04; EDSS 0-1: 0.86 ± 0.03 [P = .02]; EDSS 1.5-3: 0.95 ± 0.04 [P < .001]; EDSS ≥3.5: 0.94 ± 0.04 [P = .001]). A lower ratio of NAA to tCr was found in MS subgroups with disabilities, both in their NAWM (marginal mean ± standard error, healthy controls: 1.46 ± 0.04; EDSS 1.5-3: 1.33 ± 0.03 [P = .03]; EDSS ≥3.5: 1.30 ± 0.04 [P = .01]) and CGM (marginal mean ± standard error, healthy controls: 1.42 ± 0.05; EDSS ≥3.5: 1.23 ± 0.05 [P = .006]). mI/NAA correlated with EDSS (NAWM of centrum semiovale: r = 0.47, P < .001; parietal NAWM: r = 0.43, P = .002; frontal NAWM: r = 0.34, P = .01; frontal CGM: r = 0.37, P = .004). Conclusion MR spectroscopic imaging at 7.0 T allowed in vivo visualization of multiple sclerosis pathologic findings not visible at T1- or T2-weighted MRI. Metabolic abnormalities in the normal-appearing white matter and cortical gray matter were associated with disability. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Barker in this issue.
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Affiliation(s)
- Eva Heckova
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Assunta Dal-Bianco
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Bernhard Strasser
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Gilbert J Hangel
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Alexandra Lipka
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Stanislav Motyka
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Lukas Hingerl
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Paulus S Rommer
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Thomas Berger
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Petra Hnilicová
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Ema Kantorová
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Fritz Leutmezer
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Egon Kurča
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Stephan Gruber
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Siegfried Trattnig
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
| | - Wolfgang Bogner
- From the High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090 Vienna, Austria (E.H., B.S., G.J.H., A.L., S.M., L.H., S.G., S.T., W.B.); Departments of Neurology (A.D.B., P.S.R., T.B., F.L.) and Neurosurgery (G.J.H.), Medical University of Vienna, Vienna, Austria; Biomedical Center Martin (P.H.) and Clinic of Neurology (E. Kantorová, E. Kurča), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia; and Karl Landsteiner Institute for Clinical Molecular MRI in Musculoskeletal System, Vienna, Austria (S.T.)
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24
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Strasser B, Arango NS, Stockmann JP, Gagoski B, Thapa B, Li X, Bogner W, Moser P, Small J, Cahill DP, Batchelor TT, Dietrich J, van der Kouwe A, White J, Adalsteinsson E, Andronesi OC. Improving D-2-hydroxyglutarate MR spectroscopic imaging in mutant isocitrate dehydrogenase glioma patients with multiplexed RF-receive/B 0 -shim array coils at 3 T. NMR Biomed 2022; 35:e4621. [PMID: 34609036 PMCID: PMC8717863 DOI: 10.1002/nbm.4621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 08/27/2021] [Accepted: 08/30/2021] [Indexed: 06/13/2023]
Abstract
MR spectroscopic imaging (MRSI) noninvasively maps the metabolism of human brains. In particular, the imaging of D-2-hydroxyglutarate (2HG) produced by glioma isocitrate dehydrogenase (IDH) mutations has become a key application in neuro-oncology. However, the performance of full field-of-view MRSI is limited by B0 spatial nonuniformity and lipid artifacts from tissues surrounding the brain. Array coils that multiplex RF-receive and B0 -shim electrical currents (AC/DC mixing) over the same conductive loops provide many degrees of freedom to improve B0 uniformity and reduce lipid artifacts. AC/DC coils are highly efficient due to compact design, requiring low shim currents (<2 A) that can be switched fast (0.5 ms) with high interscan reproducibility (10% coefficient of variation for repeat measurements). We measured four tumor patients and five volunteers at 3 T and show that using AC/DC coils in addition to the vendor-provided second-order spherical harmonics shim provides 19% narrower spectral linewidth, 6% higher SNR, and 23% less lipid content for unrestricted field-of-view MRSI, compared with the vendor-provided shim alone. We demonstrate that improvement in MRSI data quality led to 2HG maps with higher contrast-to-noise ratio for tumors that coincide better with the FLAIR-enhancing lesions in mutant IDH glioma patients. Smaller Cramér-Rao lower bounds for 2HG quantification are obtained in tumors by AC/DC shim, corroborating with simulations that predicted improved accuracy and precision for narrower linewidths. AC/DC coils can be used synergistically with optimized acquisition schemes to improve metabolic imaging for precision oncology of glioma patients. Furthermore, this methodology has broad applicability to other neurological disorders and neuroscience.
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Affiliation(s)
- Bernhard Strasser
- A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Radiology, Boston, Massachusetts, USA
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Vienna, Austria
| | - Nicolas S. Arango
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jason P. Stockmann
- A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Radiology, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Borjan Gagoski
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Bijaya Thapa
- A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Radiology, Boston, Massachusetts, USA
| | - Xianqi Li
- A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Radiology, Boston, Massachusetts, USA
| | - Wolfgang Bogner
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Vienna, Austria
| | - Philipp Moser
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Vienna, Austria
| | - Julia Small
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Daniel P. Cahill
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Tracy T. Batchelor
- Department Neurology, Division of Neuro-Oncology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jorg Dietrich
- Department Neurology, Division of Neuro-Oncology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Andre van der Kouwe
- A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Radiology, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Jacob White
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Ovidiu C. Andronesi
- A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Radiology, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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25
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Hangel G, Spurny-Dworak B, Lazen P, Cadrien C, Sharma S, Hingerl L, Hečková E, Strasser B, Motyka S, Lipka A, Gruber S, Brandner C, Lanzenberger R, Rössler K, Trattnig S, Bogner W. Inter-subject stability and regional concentration estimates of 3D-FID-MRSI in the human brain at 7 T. NMR Biomed 2021; 34:e4596. [PMID: 34382280 DOI: 10.1002/nbm.4596] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 07/14/2021] [Accepted: 07/14/2021] [Indexed: 05/13/2023]
Abstract
PURPOSE Recently, a 3D-concentric ring trajectory (CRT)-based free induction decay (FID)-MRSI sequence was introduced for fast high-resolution metabolic imaging at 7 T. This technique provides metabolic ratio maps of almost the entire brain within clinically feasible scan times, but its robustness has not yet been thoroughly investigated. Therefore, we have assessed quantitative concentration estimates and their variability in healthy volunteers using this approach. METHODS We acquired whole-brain 3D-CRT-FID-MRSI at 7 T in 15 min with 3.4 mm nominal isometric resolution in 24 volunteers (12 male, 12 female, mean age 27 ± 6 years). Concentration estimate maps were calculated for 15 metabolites using internal water referencing and evaluated in 55 different regions of interest (ROIs) in the brain. Data quality, mean metabolite concentrations, and their inter-subject coefficients of variation (CVs) were compared for all ROIs. RESULTS Of 24 datasets, one was excluded due to motion artifacts. The concentrations of total choline, total creatine, glutamate, myo-inositol, and N-acetylaspartate in 44 regions were estimated within quality thresholds. Inter-subject CVs (mean over 44 ROIs/minimum/maximum) were 9%/5%/19% for total choline, 10%/6%/20% for total creatine, 11%/7%/24% for glutamate, 10%/6%/19% for myo-inositol, and 9%/6%/19% for N-acetylaspartate. DISCUSSION We defined the performance of 3D-CRT-based FID-MRSI for metabolite concentration estimate mapping, showing which metabolites could be robustly quantified in which ROIs with which inter-subject CVs expected. However, the basal brain regions and lesser-signal metabolites in particular remain as a challenge due susceptibility effects from the proximity to nasal and auditory cavities. Further improvement in quantification and the mitigation of B0 /B1 -field inhomogeneities will be necessary to achieve reliable whole-brain coverage.
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Affiliation(s)
- Gilbert Hangel
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Benjamin Spurny-Dworak
- Division of General Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Philipp Lazen
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Cornelius Cadrien
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Sukrit Sharma
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lukas Hingerl
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Eva Hečková
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Bernhard Strasser
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Stanislav Motyka
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Alexandra Lipka
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Institute for Clinical Molecular MRI, Karl Landsteiner Society, St. Pölten, Austria
| | - Stephan Gruber
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Christoph Brandner
- High-field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Division of General Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Karl Rössler
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Siegfried Trattnig
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Institute for Clinical Molecular MRI, Karl Landsteiner Society, St. Pölten, Austria
| | - Wolfgang Bogner
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
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Dong S, Hangel G, Bogner W, Trattnig S, Rossler K, Widhalm G, De Feyter HM, De Graaf RA, Duncan JS. High-Resolution Magnetic Resonance Spectroscopic Imaging using a Multi-Encoder Attention U-Net with Structural and Adversarial Loss. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:2891-2895. [PMID: 34891851 DOI: 10.1109/embc46164.2021.9630146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Common to most medical imaging techniques, the spatial resolution of Magnetic Resonance Spectroscopic Imaging (MRSI) is ultimately limited by the achievable SNR. This work presents a deep learning method for 1H-MRSI spatial resolution enhancement, based on the observation that multi-parametric MRI images provide relevant spatial priors for MRSI enhancement. A Multi-encoder Attention U-Net (MAU-Net) architecture was constructed to process a MRSI metabolic map and three different MRI modalities through separate encoding paths. Spatial attention modules were incorporated to automatically learn spatial weights that highlight salient features for each MRI modality. MAU-Net was trained based on in vivo brain imaging data from patients with high-grade gliomas, using a combined loss function consisting of pixel, structural and adversarial loss. Experimental results showed that the proposed method is able to reconstruct high-quality metabolic maps with a high-resolution of 64×64 from a low-resolution of 16 × 16, with better performance compared to several baseline methods.
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Hui SCN, Mikkelsen M, Zöllner HJ, Ahluwalia V, Alcauter S, Baltusis L, Barany DA, Barlow LR, Becker R, Berman JI, Berrington A, Bhattacharyya PK, Blicher JU, Bogner W, Brown MS, Calhoun VD, Castillo R, Cecil KM, Choi YB, Chu WCW, Clarke WT, Craven AR, Cuypers K, Dacko M, de la Fuente-Sandoval C, Desmond P, Domagalik A, Dumont J, Duncan NW, Dydak U, Dyke K, Edmondson DA, Ende G, Ersland L, Evans CJ, Fermin ASR, Ferretti A, Fillmer A, Gong T, Greenhouse I, Grist JT, Gu M, Harris AD, Hat K, Heba S, Heckova E, Hegarty JP, Heise KF, Honda S, Jacobson A, Jansen JFA, Jenkins CW, Johnston SJ, Juchem C, Kangarlu A, Kerr AB, Landheer K, Lange T, Lee P, Levendovszky SR, Limperopoulos C, Liu F, Lloyd W, Lythgoe DJ, Machizawa MG, MacMillan EL, Maddock RJ, Manzhurtsev AV, Martinez-Gudino ML, Miller JJ, Mirzakhanian H, Moreno-Ortega M, Mullins PG, Nakajima S, Near J, Noeske R, Nordhøy W, Oeltzschner G, Osorio-Duran R, Otaduy MCG, Pasaye EH, Peeters R, Peltier SJ, Pilatus U, Polomac N, Porges EC, Pradhan S, Prisciandaro JJ, Puts NA, Rae CD, Reyes-Madrigal F, Roberts TPL, Robertson CE, Rosenberg JT, Rotaru DG, O'Gorman Tuura RL, Saleh MG, Sandberg K, Sangill R, Schembri K, Schrantee A, Semenova NA, Singel D, Sitnikov R, Smith J, Song Y, Stark C, Stoffers D, Swinnen SP, Tain R, Tanase C, Tapper S, Tegenthoff M, Thiel T, Thioux M, Truong P, van Dijk P, Vella N, Vidyasagar R, Vovk A, Wang G, Westlye LT, Wilbur TK, Willoughby WR, Wilson M, Wittsack HJ, Woods AJ, Wu YC, Xu J, Lopez MY, Yeung DKW, Zhao Q, Zhou X, Zupan G, Edden RAE. Frequency drift in MR spectroscopy at 3T. Neuroimage 2021; 241:118430. [PMID: 34314848 PMCID: PMC8456751 DOI: 10.1016/j.neuroimage.2021.118430] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 06/18/2021] [Accepted: 07/22/2021] [Indexed: 11/17/2022] Open
Abstract
PURPOSE Heating of gradient coils and passive shim components is a common cause of instability in the B0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. METHOD A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC). RESULTS Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. DISCUSSION This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.
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Affiliation(s)
- Steve C N Hui
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Mark Mikkelsen
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Helge J Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Vishwadeep Ahluwalia
- GSU/GT Center for Advanced Brain Imaging, Georgia Institute of Technology, Atlanta, GA USA
| | - Sarael Alcauter
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Queretaro, Mexico
| | - Laima Baltusis
- Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA USA
| | - Deborah A Barany
- Department of Kinesiology, University of Georgia, and Augusta University/University of Georgia Medical Partnership, Athens, GA USA
| | - Laura R Barlow
- Department of Radiology, Faculty of Medicine, The University of British Columbia, Vancouver, Canada
| | - Robert Becker
- Center for Innovative Psychiatry and Psychotherapy Research, Department Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jeffrey I Berman
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA USA
| | - Adam Berrington
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | | | - Jakob Udby Blicher
- Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Wolfgang Bogner
- Department of Biomedical Imaging and Image-guided Therapy, High-Field MR Center, Medical University of Vienna, Vienna, Austria
| | - Mark S Brown
- Department of Radiology, Medical Physics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA USA
| | - Ryan Castillo
- NeuRA Imaging, Neuroscience Research Australia, Randwick, Australia
| | - Kim M Cecil
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA
| | - Yeo Bi Choi
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH USA
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Alexander R Craven
- Department of Biological and Medical Psychology, University of Bergen, Haukeland University Hospital, Bergen, Norway
| | - Koen Cuypers
- REVAL Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium; Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Michael Dacko
- Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Camilo de la Fuente-Sandoval
- Laboratory of Experimental Psychiatry & Neuropsychiatry Department, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Patricia Desmond
- Department of Radiology, University of Melbourne/ Royal Melbourne Hospital, Melbourne, Australia
| | - Aleksandra Domagalik
- Brain Imaging Core Facility, Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Julien Dumont
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UMS 2014 - PLBS, F-59000 Lille, France
| | - Niall W Duncan
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
| | - Ulrike Dydak
- School of Health Sciences, Purdue University, West Lafayette, IN USA
| | - Katherine Dyke
- School of Psychology, University of Nottingham, Nottingham, UK
| | - David A Edmondson
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA
| | - Gabriele Ende
- Center for Innovative Psychiatry and Psychotherapy Research, Department Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lars Ersland
- Department of Clinical Engineering, University of Bergen, Haukeland University Hospital, Bergen, Norway
| | | | - Alan S R Fermin
- Center for Brain, Mind and KANSEI Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Antonio Ferretti
- Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Ariane Fillmer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig und Berlin, Germany
| | - Tao Gong
- Department of Imaging and Nuclear Medicine, Shandong Medical Imaging Research Institute, Shandong University, Jinan, China
| | - Ian Greenhouse
- Department of Human Physiology, University of Oregon, Eugene, OR USA
| | - James T Grist
- Department of Physiology, Anatomy, and Genetics, Oxford Centre for Magnetic Resonance / Department of Radiology, The Churchill Hospital, The University of Oxford, Oxford, UK
| | - Meng Gu
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Ashley D Harris
- Department of Radiology, University of Calgary, Calgary, Canada
| | - Katarzyna Hat
- Consciousness Lab, Institute of Psychology, Jagiellonian University, Kraków, Poland
| | - Stefanie Heba
- Department of Neurology, BG University Hospital Bergmannsheil, Bochum, Germany
| | - Eva Heckova
- Department of Biomedical Imaging and Image-guided Therapy, High-Field MR Center, Medical University of Vienna, Vienna, Austria
| | - John P Hegarty
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
| | | | - Shiori Honda
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Aaron Jacobson
- Department of Radiology / Psychiatry, University of California San Diego, San Diego, CA USA
| | - Jacobus F A Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | | | - Stephen J Johnston
- Psychology Department / Clinical Imaging Facility, Swansea University, Swansea, UK
| | - Christoph Juchem
- Departments of Biomedical Engineering and Radiology, Columbia University, New York, NY USA
| | - Alayar Kangarlu
- Department of Psychiatry, Columbia University Irving Medical Center/New York State Psychiatric Institute, New York, NY USA
| | - Adam B Kerr
- Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA USA
| | - Karl Landheer
- Departments of Biomedical Engineering and Radiology, Columbia University, New York, NY USA
| | - Thomas Lange
- Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Phil Lee
- Department of Radiology / Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS USA
| | | | - Catherine Limperopoulos
- Developing Brain Institute, Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC USA
| | - Feng Liu
- Department of Psychiatry, Columbia University Irving Medical Center/New York State Psychiatric Institute, New York, NY USA
| | - William Lloyd
- Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK
| | - David J Lythgoe
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Maro G Machizawa
- Center for Brain, Mind and KANSEI Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Erin L MacMillan
- Department of Radiology, Faculty of Medicine, The University of British Columbia, Vancouver, Canada; Philips Canada, Markham, ON, Canada
| | - Richard J Maddock
- Department of Psychiatry and Behavioral Sciences, University of California Davis, Imaging Research Center, Davis, CA USA
| | - Andrei V Manzhurtsev
- Department of Radiology, Clinical and Research Institute of Emergency Pediatric Surgery and Trauma, Moscow, Russia; Emanuel Institute of Biochemical Physics of the Russian Academy of Sciences, Moscow, Russia
| | - María L Martinez-Gudino
- Departamento de Imágenes Cerebrales, Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz, Mexico City, Mexico
| | - Jack J Miller
- Department of Physics, University of Oxford, Oxford, UK; The MR Research Centre & The PET Research Centre, Aarhus University, Aarhus, DK
| | - Heline Mirzakhanian
- Department of Radiology / Psychiatry, University of California San Diego, San Diego, CA USA
| | - Marta Moreno-Ortega
- Department of Psychiatry, Columbia University Irving Medical Center/New York State Psychiatric Institute, New York, NY USA
| | - Paul G Mullins
- Bangor Imaging Unit, Department of Psychology, Bangor University, Bangor, Wales, UK
| | - Shinichiro Nakajima
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Jamie Near
- Douglas Mental Health University Institute and Department of Psychiatry, McGill University, Montreal, Canada
| | | | - Wibeke Nordhøy
- NORMENT, Division of Mental Health and Addiction and Department of Diagnostic Physics, Division of Radiology and Nuclear Medicine, Oslo University Hospital / Department of Psychology, University of Oslo, Oslo, Norway
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Raul Osorio-Duran
- Departamento de Imágenes Cerebrales, Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz, Mexico City, Mexico
| | - Maria C G Otaduy
- LIM44, Instituto e Departamento de Radiologia, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Erick H Pasaye
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Queretaro, Mexico
| | - Ronald Peeters
- Department of Imaging & Pathology, Department of Radiology, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Scott J Peltier
- Functional MRI Laboratory, University of Michigan, Ann Arbor, MI USA
| | - Ulrich Pilatus
- Institute of Neuroradiology, Goethe-University Frankfurt, Frankfurt, Germany
| | - Nenad Polomac
- Institute of Neuroradiology, Goethe-University Frankfurt, Frankfurt, Germany
| | - Eric C Porges
- Center for Cognitive Aging and Memory, McKnight Brain Institute, Department of Clinical and Health Psychology, College of Public Health and Health Professions. Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA
| | - Subechhya Pradhan
- Developing Brain Institute, Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC USA
| | - James Joseph Prisciandaro
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC USA
| | - Nicolaas A Puts
- Department of Forensic & Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment, King's College London, London, UK
| | - Caroline D Rae
- NeuRA Imaging, Neuroscience Research Australia, Randwick, Australia
| | - Francisco Reyes-Madrigal
- Laboratory of Experimental Psychiatry & Neuropsychiatry Department, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Timothy P L Roberts
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA USA
| | - Caroline E Robertson
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH USA
| | - Jens T Rosenberg
- McKnight Brain Institute, AMRIS, University of Florida, Gainesville, FL USA
| | - Diana-Georgiana Rotaru
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Ruth L O'Gorman Tuura
- Center for MR Research, University Children's Hospital, Zurich, University of Zurich, Switzerland
| | - Muhammad G Saleh
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, USA
| | - Kristian Sandberg
- Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Ryan Sangill
- Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | | | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Natalia A Semenova
- Department of Radiology, Clinical and Research Institute of Emergency Pediatric Surgery and Trauma, Moscow, Russia; Emanuel Institute of Biochemical Physics of the Russian Academy of Sciences, Moscow, Russia
| | - Debra Singel
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Rouslan Sitnikov
- Clinical Neuroscience, MRI Centre, Karolinska Institute, Stockholm, Sweden
| | - Jolinda Smith
- Lewis Center for Neuroimaging, University of Oregon, Eugene, OR USA
| | - Yulu Song
- Department of Imaging and Nuclear Medicine, Shandong Medical Imaging Research Institute, Shandong University, Jinan, China
| | - Craig Stark
- Department of Neurobiology and Behavior, Facility for Imaging and Brain Research (FIBRE) & Campus Center for Neuroimaging (CCNI), School of Biological Sciences, University of California, Irvine, Irvine, CA USA
| | - Diederick Stoffers
- Spinoza Centre for Neuroimaging, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | | | - Rongwen Tain
- Department of Neurobiology and Behavior, Facility for Imaging and Brain Research (FIBRE) & Campus Center for Neuroimaging (CCNI), School of Biological Sciences, University of California, Irvine, Irvine, CA USA
| | - Costin Tanase
- Department of Psychiatry and Behavioral Sciences, University of California Davis, Imaging Research Center, Davis, CA USA
| | - Sofie Tapper
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Martin Tegenthoff
- Department of Neurology, BG University Hospital Bergmannsheil, Bochum, Germany
| | - Thomas Thiel
- Institute of Clinical Neuroscience and Medical Psychology, University Dusseldorf, Medical Faculty, Düsseldorf, Germany
| | - Marc Thioux
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Peter Truong
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, Canada
| | - Pim van Dijk
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Nolan Vella
- Medical Physics, Mater Dei Hospital, Imsida, Malta
| | - Rishma Vidyasagar
- Melbourne Dementia Research Centre, Florey Institute of Neurosciences and Mental Health, Melbourne, Australia
| | - Andrej Vovk
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Guangbin Wang
- Department of Imaging and Nuclear Medicine, Shandong Medical Imaging Research Institute, Shandong University, Jinan, China
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction and Department of Diagnostic Physics, Division of Radiology and Nuclear Medicine, Oslo University Hospital / Department of Psychology, University of Oslo, Oslo, Norway
| | - Timothy K Wilbur
- Department of Radiology, University of Washington, Seattle, WA USA
| | - William R Willoughby
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL USA
| | - Martin Wilson
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Hans-Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, University Düsseldorf, Medical Faculty, Düsseldorf, Germany
| | - Adam J Woods
- Center for Cognitive Aging and Memory, McKnight Brain Institute, Department of Clinical and Health Psychology, College of Public Health and Health Professions. Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA
| | - Yen-Chien Wu
- Department of Radiology, TMU-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Junqian Xu
- Department of Radiology and Psychiatry, Baylor College of Medicine, Houston, USA
| | | | - David K W Yeung
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Qun Zhao
- Bioimaging Research Center, Department of Physics and Astronomy, University of Georgia, Athens, GA USA
| | - Xiaopeng Zhou
- School of Health Sciences, Purdue University, West Lafayette, IN USA
| | - Gasper Zupan
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
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Niess F, Roat S, Bogner W, Krššák M, Kemp GJ, Schmid AI, Trattnig S, Moser E, Zaitsev M, Meyerspeer M. 3D localized lactate detection in muscle tissue using double-quantum filtered 1 H MRS with adiabatic refocusing pulses at 7 T. Magn Reson Med 2021; 87:1174-1183. [PMID: 34719061 DOI: 10.1002/mrm.29061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 10/06/2021] [Accepted: 10/07/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE Lactate is a key metabolite in skeletal muscle and whole-body physiology. Its MR visibility in muscle is affected by overlapping lipid signals and fiber orientation. Double-quantum filtered (DQF) 1 H MRS selectively detects lactate at 1.3 ppm, but at ultra-high field the efficiency of slice-selective 3D-localization with conventional RF pulses is limited by bandwidth. This novel 3D-localized 1 H DQF MRS sequence uses adiabatic refocusing pulses to unambiguously detect lactate in skeletal muscle at 7 T. METHODS Lactate double-quantum coherences were 3D-localized using slice-selective Shinnar-Le Roux optimized excitation and adiabatic refocusing pulses (similar to semi-LASER). DQF MR spectra were acquired at 7 T from lactate phantoms, meat specimens with injected lactate (exploring multiple TEs and fiber orientations), and human gastrocnemius in vivo during and after exercise (without cuff ischemia). RESULTS Lactate was readily detected, achieving the full potential of 50% signal with a DQF, in solution. The effects of fiber orientation and TE on the lactate doublet (peak splitting, amplitude, and phase) were in good agreement with theory and literature. Exercise-induced lactate accumulation was detected with 30 s time resolution. CONCLUSION This novel 3D-localized 1 H DQF MRS sequence can dynamically detect glycolytically generated lactate in muscle during exercise and recovery at 7 T.
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Affiliation(s)
- Fabian Niess
- Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Vienna, Austria.,High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Sigrun Roat
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Martin Krššák
- Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Vienna, Austria
| | - Graham J Kemp
- Department of Musculoskeletal and Ageing Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Albrecht I Schmid
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Siegfried Trattnig
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Ewald Moser
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Maxim Zaitsev
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Martin Meyerspeer
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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Ijare OB, Hambarde S, Brasil da Costa FH, Lopez S, Sharpe MA, Helekar SA, Hangel G, Bogner W, Widhalm G, Bachoo RM, Baskin DS, Pichumani K. Glutamine anaplerosis is required for amino acid biosynthesis in human meningiomas. Neuro Oncol 2021; 24:556-568. [PMID: 34515312 DOI: 10.1093/neuonc/noab219] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND We postulate that meningiomas undergo distinct metabolic reprogramming in tumorigenesis and unravelling their metabolic phenotypes provide new therapeutic insights. Glutamine catabolism is key to the growth and proliferation of tumors. Here, we investigated the metabolomics of freshly resected meningiomas and glutamine metabolism in patient-derived meningioma cells. METHODS 1H NMR spectroscopy of tumor tissues from 33 meningioma patients was used to differentiate the metabolite profiles of grade-I and grade-II meningiomas. Glutamine metabolism was examined using 13C/ 15N glutamine tracer, in five patient-derived meningioma cells. RESULTS Alanine, lactate, glutamate, glutamine, and glycine were predominantly elevated only in grade-II meningiomas by 74%, 76%, 35%, 75% and 33% respectively, with alanine, and glutamine being statistically significant (p ≤ 0.02). 13C/ 15N glutamine tracer experiments revealed that both grade-I and -II meningiomas actively metabolize glutamine to generate various key carbon intermediates including alanine and proline that are necessary for the tumor growth. Also, it is shown that glutaminase (GLS1) inhibitor, CB-839 is highly effective in downregulating glutamine metabolism and decreasing proliferation in meningioma cells. CONCLUSION Alanine and glutamine/glutamate are mainly elevated in grade-II meningiomas. Grade-I meningiomas possess relatively higher glutamine metabolism providing carbon/nitrogen for the biosynthesis of key nonessential amino acids. GLS1 inhibitor (CB-839) would be very effective in downregulating glutamine metabolic pathways in grade-I meningiomas leading to decreased cellular proliferation.
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Affiliation(s)
- Omkar B Ijare
- Kenneth R. Peak Brain and Pituitary Tumor Treatment Center, Department of Neurosurgery, Houston Methodist Neurological Institute, Houston Methodist Hospital and Research Institute, Houston, TX, USA
| | - Shashank Hambarde
- Kenneth R. Peak Brain and Pituitary Tumor Treatment Center, Department of Neurosurgery, Houston Methodist Neurological Institute, Houston Methodist Hospital and Research Institute, Houston, TX, USA
| | - Fabio Henrique Brasil da Costa
- Kenneth R. Peak Brain and Pituitary Tumor Treatment Center, Department of Neurosurgery, Houston Methodist Neurological Institute, Houston Methodist Hospital and Research Institute, Houston, TX, USA
| | - Sophie Lopez
- Kenneth R. Peak Brain and Pituitary Tumor Treatment Center, Department of Neurosurgery, Houston Methodist Neurological Institute, Houston Methodist Hospital and Research Institute, Houston, TX, USA
| | - Martyn A Sharpe
- Kenneth R. Peak Brain and Pituitary Tumor Treatment Center, Department of Neurosurgery, Houston Methodist Neurological Institute, Houston Methodist Hospital and Research Institute, Houston, TX, USA
| | - Santosh A Helekar
- Kenneth R. Peak Brain and Pituitary Tumor Treatment Center, Department of Neurosurgery, Houston Methodist Neurological Institute, Houston Methodist Hospital and Research Institute, Houston, TX, USA.,Weill Cornell Medical College, New York, NY, USA
| | - Gilbert Hangel
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.,Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Georg Widhalm
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Robert M Bachoo
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX, USA
| | - David S Baskin
- Kenneth R. Peak Brain and Pituitary Tumor Treatment Center, Department of Neurosurgery, Houston Methodist Neurological Institute, Houston Methodist Hospital and Research Institute, Houston, TX, USA.,Weill Cornell Medical College, New York, NY, USA
| | - Kumar Pichumani
- Kenneth R. Peak Brain and Pituitary Tumor Treatment Center, Department of Neurosurgery, Houston Methodist Neurological Institute, Houston Methodist Hospital and Research Institute, Houston, TX, USA.,Weill Cornell Medical College, New York, NY, USA
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30
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Gajdošík M, Landheer K, Swanberg KM, Adlparvar F, Madelin G, Bogner W, Juchem C, Kirov II. Hippocampal single-voxel MR spectroscopy with a long echo time at 3 T using semi-LASER sequence. NMR Biomed 2021; 34:e4538. [PMID: 33956374 PMCID: PMC10874619 DOI: 10.1002/nbm.4538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 04/01/2021] [Accepted: 04/19/2021] [Indexed: 06/12/2023]
Abstract
The hippocampus is one of the most challenging brain regions for proton MR spectroscopy (MRS) applications. Moreover, quantification of J-coupled species such as myo-inositol (m-Ins) and glutamate + glutamine (Glx) is affected by the presence of macromolecular background. While long echo time (TE) MRS eliminates the macromolecules, it also decreases the m-Ins and Glx signal and, as a result, these metabolites are studied mainly with short TE. Here, we investigate the feasibility of reproducibly measuring their concentrations at a long TE of 120 ms, using a semi-adiabatic localization by adiabatic selective refocusing (sLASER) sequence, as this sequence was recently recommended as a standard for clinical MRS. Gradient offset-independent adiabatic refocusing pulses were implemented, and an optimal long TE for the detection of m-Ins and Glx was determined using the T2 relaxation times of macromolecules. Metabolite concentrations and their coefficients of variation (CVs) were obtained for a 3.4-mL voxel centered on the left hippocampus on 3-T MR systems at two different sites with three healthy subjects (aged 32.5 ± 10.2 years [mean ± standard deviation]) per site, with each subject scanned over two sessions, and with each session comprising three scans. Concentrations of m-Ins, choline, creatine, Glx and N-acetyl-aspartate were 5.4 ± 1.5, 1.7 ± 0.2, 5.8 ± 0.3, 11.6 ± 1.2 and 5.9 ± 0.4 mM (mean ± standard deviation), respectively. Their respective mean within-session CVs were 14.5% ± 5.9%, 6.5% ± 5.3%, 6.0% ± 3.4%, 10.6% ± 6.2% and 3.5% ± 1.4%, and their mean within-subject CVs were 17.8% ± 18.2%, 7.5% ± 6.3%, 7.4% ± 6.4%, 12.4% ± 5.3% and 4.8% ± 3.0%. The between-subject CVs were 25.0%, 12.3%, 5.3%, 10.7% and 6.4%, respectively. Hippocampal long-TE sLASER single voxel spectroscopy can provide macromolecule-independent assessment of all major metabolites including Glx and m-Ins.
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Affiliation(s)
- Martin Gajdošík
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States
| | - Karl Landheer
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States
| | - Kelley M. Swanberg
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States
| | - Fatemeh Adlparvar
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
| | - Guillaume Madelin
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
| | - Wolfgang Bogner
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Christoph Juchem
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, United States
| | - Ivan I. Kirov
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, United States
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31
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Kantorová E, Hnilicová P, Bogner W, Grendár M, Grossmann J, Kováčová S, Hečková E, Strasser B, Čierny D, Zeleňák K, Kurča E. Neurocognitive performance in relapsing-remitting multiple sclerosis patients is associated with metabolic abnormalities of the thalamus but not the hippocampus- GABA-edited 1H MRS study. Neurol Res 2021; 44:57-64. [PMID: 34313578 DOI: 10.1080/01616412.2021.1956282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
OBJECTIVES Multiple sclerosis (MS) is an inflammatory demyelinating disease that may cause physical disabling as well as cognitive dysfunction. The presented study investigated how the neuropsychological status depends on the thalamus and hippocampus's metabolic processes, using γ-aminobutyric acid-edited magnetic resonance spectroscopy (GABA-edited 1H MRS) in patients with early MS, and how the results differ from healthy volunteers. METHODS We recruited 36 relapsing-remitting (RRMS) MS patients and 22 controls (CON). In addition to common 1H MRS metabolites, such as N-acetyl-aspartate (tNAA), myoinositol (mIns), total choline and creatine (tCr, tCho), we also evaluated GABA and glutamate/glutamine (Glx). Metabolite ratios were correlated with the results of Single-Digit Modality Test (SDMT) and Expanded Disability Status Score (EDSS). RESULTS In the thalamus, GABA ratios (GABA/tCr, GABA/tNAA) were significantly lower in RRMS patients than in CON. Both tCho- and mIns-ratios correlated with lower scores of SDMT but not with EDSS. Metabolic ratios in the hippocampus did not differ between RRMS and CON and did not correlate with any of performed tests. DISCUSSION This study is the first to provide GABA-edited 1H MRS evidence for MS-related metabolic changes of the thalamus and hippocampus. The findings underline the importance of evaluating subcortical grey matter in MS patients to improve understanding of the clinical manifestations of MS and as a potential future target for treatment.
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Affiliation(s)
- Ema Kantorová
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
| | - Petra Hnilicová
- Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
| | - Wolfgang Bogner
- Department of Biomedical Imaging and Image-Guided Therapy, High-field MR Center, Medical University of Vienna, Austria
| | - Marián Grendár
- Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
| | - Ján Grossmann
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
| | - Slavomíra Kováčová
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
| | - Eva Hečková
- Department of Biomedical Imaging and Image-Guided Therapy, High-field MR Center, Medical University of Vienna, Austria
| | - Bernhard Strasser
- Department of Biomedical Imaging and Image-Guided Therapy, High-field MR Center, Medical University of Vienna, Austria
| | - Daniel Čierny
- Department of Clinical Biochemistry, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
| | - Kamil Zeleňák
- Clinic of Radiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
| | - Egon Kurča
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
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Motyka S, Hingerl L, Strasser B, Hangel G, Heckova E, Agibetov A, Dorffner G, Gruber S, Trattning S, Bogner W. k-Space-based coil combination via geometric deep learning for reconstruction of non-Cartesian MRSI data. Magn Reson Med 2021; 86:2353-2367. [PMID: 34061405 DOI: 10.1002/mrm.28876] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 05/06/2021] [Accepted: 05/07/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE State-of-the-art whole-brain MRSI with spatial-spectral encoding and multichannel acquisition generates huge amounts of data, which must be efficiently processed to stay within reasonable reconstruction times. Although coil combination significantly reduces the amount of data, currently it is performed in image space at the end of the reconstruction. This prolongs reconstruction times and increases RAM requirements. We propose an alternative k-space-based coil combination that uses geometric deep learning to combine MRSI data already in native non-Cartesian k-space. METHODS Twelve volunteers were scanned at a 3T MR scanner with a 20-channel head coil at 10 different positions with water-unsuppressed MRSI. At the eleventh position, water-suppressed MRSI data were acquired. Data of 7 volunteers were used to estimate sensitivity maps and form a base for simulating training data. A neural network was designed and trained to remove the effect of sensitivity profiles of the coil elements from the MRSI data. The water-suppressed MRSI data of the remaining volunteers were used to evaluate the performance of the new k-space-based coil combination relative to that of a conventional image-based alternative. RESULTS For both approaches, the resulting metabolic ratio maps were similar. The SNR of the k-space-based approach was comparable to the conventional approach in low SNR regions, but underperformed for high SNR. The Cramér-Rao lower bounds show the same trend. The analysis of the FWHM showed no difference between the two methods. CONCLUSION k-Space-based coil combination of MRSI data is feasible and reduces the amount of raw data immediately after their sampling.
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Affiliation(s)
- Stanislav Motyka
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lukas Hingerl
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Bernhard Strasser
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Gilbert Hangel
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.,Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Eva Heckova
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Asan Agibetov
- Section for Artificial Intelligence and Decision Support (CeMSIIS), Medical University of Vienna, Vienna, Austria
| | - Georg Dorffner
- Section for Artificial Intelligence and Decision Support (CeMSIIS), Medical University of Vienna, Vienna, Austria
| | - Stephan Gruber
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Siegfried Trattning
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.,Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
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33
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Slobodová L, Oreská Ľ, Schön M, Krumpolec P, Tirpáková V, Jurina P, Laurovič J, Vajda M, Nemec M, Hečková E, Šoóšová I, Cvečka J, Hamar D, Turčáni P, Tsai CL, Bogner W, Sedliak M, Krššák M, Ukropec J, Ukropcová B. Effects of Short- and Long-Term Aerobic-Strength Training and Determinants of Walking Speed in the Elderly. Gerontology 2021; 68:151-161. [PMID: 33971654 DOI: 10.1159/000515325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 02/18/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND/AIMS Walking speed (WS) is an objective measure of physical capacity and a modifiable risk factor of morbidity and mortality in the elderly. In this study, we (i) determined effects of 3-month supervised aerobic-strength training on WS, muscle strength, and habitual physical activity; (ii) evaluated capacity of long-term (21 months) training to sustain higher WS; and (iii) identified determinants of WS in the elderly. METHODS Volunteers (F 48/M 14, 68.4 ± 7.1 years) completed either 3-month aerobic-strength (3 × 1 h/week, n = 48) or stretching (active control, n = 14) intervention (study A). Thirty-one individuals (F 24/M 7) from study A continued in supervised aerobic-strength training (2 × 1 h/week, 21 months) and 6 (F 5/M 1) became nonexercising controls. RESULTS Three-month aerobic-strength training increased preferred and maximal WS (10-m walk test, p < 0.01), muscle strength (p < 0.01) and torque (p < 0.01) at knee extension, and 24-h habitual physical activity (p < 0.001), while stretching increased only preferred WS (p < 0.03). Effect of training on maximal WS was most prominent in individuals with baseline WS between 1.85 and 2.30 m·s-1. Maximal WS measured before intervention correlated negatively with age (r = -0.339, p = 0.007), but this correlation was weakened by the intervention (r = -0.238, p = 0.06). WS progressively increased within the first 9 months of aerobic-strength training (p < 0.001) and remained elevated during 21-month intervention (p < 0.01). Cerebellar gray matter volume (MRI) was positively associated with maximal (r = 0.54; p < 0.0001) but not preferred WS and explained >26% of its variability, while age had only minor effect. CONCLUSIONS Supervised aerobic-strength training increased WS, strength, and dynamics of voluntary knee extension as well as habitual physical activity in older individuals. Favorable changes in WS were sustainable over the 21-month period by a lower dose of aerobic-strength training. Training effects on WS were not limited by age, and cerebellar cortex volume was the key determinant of WS.
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Affiliation(s)
- Lucia Slobodová
- Biomedical Research Center, Institute of Experimental Endocrinology, Slovak Academy of Sciences, University Science, Park for Biomedicine, Bratislava, Slovakia.,Institute of Pathophysiology, Faculty of Medicine, Comenius University, Bratislava, Slovakia
| | - Ľudmila Oreská
- Faculty of Physical Education and Sports, Comenius University, Bratislava, Slovakia
| | - Martin Schön
- Biomedical Research Center, Institute of Experimental Endocrinology, Slovak Academy of Sciences, University Science, Park for Biomedicine, Bratislava, Slovakia.,Institute of Pathophysiology, Faculty of Medicine, Comenius University, Bratislava, Slovakia
| | - Patrik Krumpolec
- Biomedical Research Center, Institute of Experimental Endocrinology, Slovak Academy of Sciences, University Science, Park for Biomedicine, Bratislava, Slovakia.,Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
| | - Veronika Tirpáková
- Institute of Sports Medicine, Faculty of Medicine, Slovak Medical University, Bratislava, Slovakia
| | - Peter Jurina
- Biomedical Research Center, Institute of Experimental Endocrinology, Slovak Academy of Sciences, University Science, Park for Biomedicine, Bratislava, Slovakia
| | - Jakub Laurovič
- Biomedical Research Center, Institute of Experimental Endocrinology, Slovak Academy of Sciences, University Science, Park for Biomedicine, Bratislava, Slovakia
| | - Matej Vajda
- Faculty of Physical Education and Sports, Comenius University, Bratislava, Slovakia
| | - Michal Nemec
- Biomedical Research Center, Institute of Experimental Endocrinology, Slovak Academy of Sciences, University Science, Park for Biomedicine, Bratislava, Slovakia
| | - Eva Hečková
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Ivana Šoóšová
- National Institute of Cardiovascular Diseases, Bratislava, Slovakia
| | - Ján Cvečka
- Faculty of Physical Education and Sports, Comenius University, Bratislava, Slovakia
| | - Dušan Hamar
- Faculty of Physical Education and Sports, Comenius University, Bratislava, Slovakia
| | - Peter Turčáni
- 1st Department of Neurology, Faculty of Medicine, Comenius University & University Hospital Bratislava, Bratislava, Slovakia
| | - Chia-Liang Tsai
- Institute of Physical Education, Health and Leisure Studies, National Cheng Kung University, Tainan, Taiwan
| | - Wolfgang Bogner
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Milan Sedliak
- Faculty of Physical Education and Sports, Comenius University, Bratislava, Slovakia
| | - Martin Krššák
- Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria.,Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Jozef Ukropec
- Biomedical Research Center, Institute of Experimental Endocrinology, Slovak Academy of Sciences, University Science, Park for Biomedicine, Bratislava, Slovakia
| | - Barbara Ukropcová
- Biomedical Research Center, Institute of Experimental Endocrinology, Slovak Academy of Sciences, University Science, Park for Biomedicine, Bratislava, Slovakia.,Institute of Pathophysiology, Faculty of Medicine, Comenius University, Bratislava, Slovakia
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Cudalbu C, Behar KL, Bhattacharyya PK, Bogner W, Borbath T, de Graaf RA, Gruetter R, Henning A, Juchem C, Kreis R, Lee P, Lei H, Marjańska M, Mekle R, Murali-Manohar S, Považan M, Rackayová V, Simicic D, Slotboom J, Soher BJ, Starčuk Z, Starčuková J, Tkáč I, Williams S, Wilson M, Wright AM, Xin L, Mlynárik V. Contribution of macromolecules to brain 1 H MR spectra: Experts' consensus recommendations. NMR Biomed 2021; 34:e4393. [PMID: 33236818 PMCID: PMC10072289 DOI: 10.1002/nbm.4393] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 07/08/2020] [Accepted: 07/13/2020] [Indexed: 05/08/2023]
Abstract
Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper.
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Affiliation(s)
- Cristina Cudalbu
- Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Vaud, Switzerland
| | - Kevin L Behar
- Magnetic Resonance Research Center and Department of Psychiatry, Yale University, New Haven, Connecticut, USA
| | | | - Wolfgang Bogner
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
| | - Tamas Borbath
- High-Field Magnetic Resonance, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
- Faculty of Science, Eberhard-Karls Universität Tübingen, Tübingen, Germany
| | - Robin A de Graaf
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA
| | - Rolf Gruetter
- Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Anke Henning
- High-Field Magnetic Resonance, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, Germany
| | - Christoph Juchem
- Departments of Biomedical Engineering and Radiology, Columbia University, New York, USA
| | - Roland Kreis
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland
| | - Phil Lee
- Department of Radiology, Hoglund Brain Imaging Center, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Hongxia Lei
- Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Vaud, Switzerland
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Ralf Mekle
- Center for Stroke Research Berlin (CSB), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Saipavitra Murali-Manohar
- High-Field Magnetic Resonance, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
- Faculty of Science, Eberhard-Karls Universität Tübingen, Tübingen, Germany
| | - Michal Považan
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Veronika Rackayová
- Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Vaud, Switzerland
- Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Dunja Simicic
- Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Vaud, Switzerland
- Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Johannes Slotboom
- University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern and Inselspital, Bern, Switzerland
| | - Brian J Soher
- Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Zenon Starčuk
- Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czech Republic
| | - Jana Starčuková
- Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czech Republic
| | - Ivan Tkáč
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Stephen Williams
- Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Martin Wilson
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Andrew Martin Wright
- High-Field Magnetic Resonance, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, Eberhard-Karls Universität Tübingen, Tübingen, Germany
| | - Lijing Xin
- Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Vaud, Switzerland
| | - Vladimír Mlynárik
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
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35
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Bogner W, Otazo R, Henning A. Accelerated MR spectroscopic imaging-a review of current and emerging techniques. NMR Biomed 2021; 34:e4314. [PMID: 32399974 PMCID: PMC8244067 DOI: 10.1002/nbm.4314] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 03/24/2020] [Accepted: 03/30/2020] [Indexed: 05/14/2023]
Abstract
Over more than 30 years in vivo MR spectroscopic imaging (MRSI) has undergone an enormous evolution from theoretical concepts in the early 1980s to the robust imaging technique that it is today. The development of both fast and efficient sampling and reconstruction techniques has played a fundamental role in this process. State-of-the-art MRSI has grown from a slow purely phase-encoded acquisition technique to a method that today combines the benefits of different acceleration techniques. These include shortening of repetition times, spatial-spectral encoding, undersampling of k-space and time domain, and use of spatial-spectral prior knowledge in the reconstruction. In this way in vivo MRSI has considerably advanced in terms of spatial coverage, spatial resolution, acquisition speed, artifact suppression, number of detectable metabolites and quantification precision. Acceleration not only has been the enabling factor in high-resolution whole-brain 1 H-MRSI, but today is also common in non-proton MRSI (31 P, 2 H and 13 C) and applied in many different organs. In this process, MRSI techniques had to constantly adapt, but have also benefitted from the significant increase of magnetic field strength boosting the signal-to-noise ratio along with high gradient fidelity and high-density receive arrays. In combination with recent trends in image reconstruction and much improved computation power, these advances led to a number of novel developments with respect to MRSI acceleration. Today MRSI allows for non-invasive and non-ionizing mapping of the spatial distribution of various metabolites' tissue concentrations in animals or humans, is applied for clinical diagnostics and has been established as an important tool for neuro-scientific and metabolism research. This review highlights the developments of the last five years and puts them into the context of earlier MRSI acceleration techniques. In addition to 1 H-MRSI it also includes other relevant nuclei and is not limited to certain body regions or specific applications.
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Affiliation(s)
- Wolfgang Bogner
- High‐Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Ricardo Otazo
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew York, New YorkUSA
| | - Anke Henning
- Max Planck Institute for Biological CyberneticsTübingenGermany
- Advanced Imaging Research Center, UT Southwestern Medical CenterDallasTexasUSA
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Ma RE, Murdoch JB, Bogner W, Andronesi O, Dydak U. Atlas-based GABA mapping with 3D MEGA-MRSI: Cross-correlation to single-voxel MRS. NMR Biomed 2021; 34:e4275. [PMID: 32078755 PMCID: PMC7438238 DOI: 10.1002/nbm.4275] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 01/11/2020] [Accepted: 01/23/2020] [Indexed: 06/10/2023]
Abstract
The purpose of this work is to develop and validate a new atlas-based metabolite quantification pipeline for edited magnetic resonance spectroscopic imaging (MEGA-MRSI) that enables group comparisons of brain structure-specific GABA levels. By using brain structure masks segmented from high-resolution MPRAGE images and coregistering these to MEGA-LASER 3D MRSI data, an automated regional quantification of neurochemical levels is demonstrated for the example of the thalamus. Thalamic gamma-aminobutyric acid + coedited macromolecules (GABA+) levels from 21 healthy subjects scanned at 3 T were cross-validated both against a single-voxel MEGA-PRESS acquisition in the same subjects and same scan sessions, as well as alternative MRSI processing techniques (ROI approach, four-voxel approach) using Pearson correlation analysis. In addition, reproducibility was compared across the MRSI processing techniques in test-retest data from 14 subjects. The atlas-based approach showed a significant correlation with SV MEGA-PRESS (correlation coefficient r [GABA+] = 0.63, P < 0.0001). However, the actual values for GABA+, NAA, tCr, GABA+/tCr and tNAA/tCr obtained from the atlas-based approach showed an offset to SV MEGA-PRESS levels, likely due to the fact that on average the thalamus mask used for the atlas-based approach only occupied 30% of the SVS volume, ie, somewhat different anatomies were sampled. Furthermore, the new atlas-based approach showed highly reproducible GABA+/tCr values with a low median coefficient of variance of 6.3%. In conclusion, the atlas-based metabolite quantification approach enables a more brain structure-specific comparison of GABA+ and other neurochemical levels across populations, even when using an MRSI technique with only cm-level resolution. This approach was successfully cross-validated against the typically used SVS technique as well as other different MRSI analysis methods, indicating the robustness of this quantification approach.
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Affiliation(s)
- Ruoyun E. Ma
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
- School of Health Sciences, Purdue University, West Lafayette, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Ovidiu Andronesi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ulrike Dydak
- School of Health Sciences, Purdue University, West Lafayette, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
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Maudsley AA, Andronesi OC, Barker PB, Bizzi A, Bogner W, Henning A, Nelson SJ, Posse S, Shungu DC, Soher BJ. Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations. NMR Biomed 2021; 34:e4309. [PMID: 32350978 PMCID: PMC7606742 DOI: 10.1002/nbm.4309] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 02/01/2020] [Accepted: 03/10/2020] [Indexed: 05/04/2023]
Abstract
Magnetic resonance spectroscopic imaging (MRSI) offers considerable promise for monitoring metabolic alterations associated with disease or injury; however, to date, these methods have not had a significant impact on clinical care, and their use remains largely confined to the research community and a limited number of clinical sites. The MRSI methods currently implemented on clinical MRI instruments have remained essentially unchanged for two decades, with only incremental improvements in sequence implementation. During this time, a number of technological developments have taken place that have already greatly benefited the quality of MRSI measurements within the research community and which promise to bring advanced MRSI studies to the point where the technique becomes a true imaging modality, while making the traditional review of individual spectra a secondary requirement. Furthermore, the increasing use of biomedical MR spectroscopy studies has indicated clinical areas where advanced MRSI methods can provide valuable information for clinical care. In light of this rapidly changing technological environment and growing understanding of the value of MRSI studies for biomedical studies, this article presents a consensus from a group of experts in the field that reviews the state-of-the-art for clinical proton MRSI studies of the human brain, recommends minimal standards for further development of vendor-provided MRSI implementations, and identifies areas which need further technical development.
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Affiliation(s)
- Andrew A Maudsley
- Department of Radiology, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Ovidiu C Andronesi
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, Massachusetts
| | - Peter B Barker
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, and the Kennedy Krieger Institute, F.M. Kirby Center for Functional Brain Imaging, Baltimore, Maryland
| | - Alberto Bizzi
- Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - Anke Henning
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Sarah J Nelson
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Stefan Posse
- Department of Neurology, University of New Mexico, Albuquerque, New Mexico
| | - Dikoma C Shungu
- Department of Neuroradiology, Weill Cornell Medical College, New York, New York
| | - Brian J Soher
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
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Andronesi OC, Bhattacharyya PK, Bogner W, Choi IY, Hess AT, Lee P, Meintjes E, Tisdall MD, Zaitzev M, van der Kouwe A. Motion correction methods for MRS: experts' consensus recommendations. NMR Biomed 2021; 34:e4364. [PMID: 33089547 PMCID: PMC7855523 DOI: 10.1002/nbm.4364] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 06/07/2020] [Accepted: 06/08/2020] [Indexed: 05/07/2023]
Abstract
Long acquisition times due to intrinsically low signal-to-noise ratio and the need for highly homogeneous B0 field make MRS particularly susceptible to motion or scanner instability compared with MRI. Motion-induced changes in both localization and shimming (ie B0 homogeneity) degrade MRS data quality. To mitigate the effects of motion three approaches can be employed: (1) subject immobilization, (2) retrospective correction, and (3) prospective real-time correction using internal and/or external tracking methods. Prospective real-time correction methods can simultaneously update localization and the B0 field to improve MRS data quality. While localization errors can be corrected with both internal (navigators) and external (optical camera, NMR probes) tracking methods, the B0 field correction requires internal navigator methods to measure the B0 field inside the imaged volume and the possibility to update the scanner shim hardware in real time. Internal and external tracking can rapidly update the MRS localization with submillimeter and subdegree precision, while scanner frequency and first-order shims of scanner hardware can be updated by internal methods every sequence repetition. These approaches are most well developed for neuroimaging, for which rigid transformation is primarily applicable. Real-time correction greatly improves the stability of MRS acquisition and quantification, as shown in clinical studies on subjects prone to motion, including children and patients with movement disorders, enabling robust measurement of metabolite signals including those with low concentrations, such as gamma-aminobutyric acid and glutathione. Thus, motion correction is recommended for MRS users and calls for tighter integration and wider availability of such methods by MR scanner manufacturers.
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Affiliation(s)
- Ovidiu C. Andronesi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Corresponding Author: Ovidiu C. Andronesi, MD, PhD, Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Thirteenth Street, Charlestown, MA 02129, USA;
| | | | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - In-Young Choi
- Department of Neurology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, USA
| | - Aaron T. Hess
- University of Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, University of Oxford
| | - Phil Lee
- Department of Radiology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, USA
| | - Ernesta Meintjes
- UCT Medical Imaging Research Unit, Division of Biomedical Engineering, Department of Human Biology, University of Cape Town
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania
| | - Maxim Zaitzev
- Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- High Field Magnetic Resonance Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - André van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Choi IY, Andronesi OC, Barker P, Bogner W, Edden RAE, Kaiser LG, Lee P, Marjańska M, Terpstra M, de Graaf RA. Spectral editing in 1 H magnetic resonance spectroscopy: Experts' consensus recommendations. NMR Biomed 2021; 34:e4411. [PMID: 32946145 PMCID: PMC8557623 DOI: 10.1002/nbm.4411] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/31/2020] [Accepted: 09/01/2020] [Indexed: 05/08/2023]
Abstract
Spectral editing in in vivo 1 H-MRS provides an effective means to measure low-concentration metabolite signals that cannot be reliably measured by conventional MRS techniques due to signal overlap, for example, γ-aminobutyric acid, glutathione and D-2-hydroxyglutarate. Spectral editing strategies utilize known J-coupling relationships within the metabolite of interest to discriminate their resonances from overlying signals. This consensus recommendation paper provides a brief overview of commonly used homonuclear editing techniques and considerations for data acquisition, processing and quantification. Also, we have listed the experts' recommendations for minimum requirements to achieve adequate spectral editing and reliable quantification. These include selecting the right editing sequence, dealing with frequency drift, handling unwanted coedited resonances, spectral fitting of edited spectra, setting up multicenter clinical trials and recommending sequence parameters to be reported in publications.
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Affiliation(s)
- In-Young Choi
- Department of Neurology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, Kansas
| | - Ovidiu C Andronesi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Peter Barker
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, F. M. Kirby Center for Functional MRI, Kennedy Krieger Institute, Baltimore, Maryland
| | - Wolfgang Bogner
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, F. M. Kirby Center for Functional MRI, Kennedy Krieger Institute, Baltimore, Maryland
| | - Lana G Kaiser
- Henry H. Wheeler, Jr. Brain Imaging Center, University of California, Berkeley, California
| | - Phil Lee
- Department of Radiology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, Kansas
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Melissa Terpstra
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Robin A de Graaf
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut
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Lin A, Andronesi O, Bogner W, Choi I, Coello E, Cudalbu C, Juchem C, Kemp GJ, Kreis R, Krššák M, Lee P, Maudsley AA, Meyerspeer M, Mlynarik V, Near J, Öz G, Peek AL, Puts NA, Ratai E, Tkáč I, Mullins PG. Minimum Reporting Standards for in vivo Magnetic Resonance Spectroscopy (MRSinMRS): Experts' consensus recommendations. NMR Biomed 2021; 34:e4484. [PMID: 33559967 PMCID: PMC8647919 DOI: 10.1002/nbm.4484] [Citation(s) in RCA: 108] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 11/24/2020] [Accepted: 01/12/2021] [Indexed: 05/08/2023]
Abstract
The translation of MRS to clinical practice has been impeded by the lack of technical standardization. There are multiple methods of acquisition, post-processing, and analysis whose details greatly impact the interpretation of the results. These details are often not fully reported, making it difficult to assess MRS studies on a standardized basis. This hampers the reviewing of manuscripts, limits the reproducibility of study results, and complicates meta-analysis of the literature. In this paper a consensus group of MRS experts provides minimum guidelines for the reporting of MRS methods and results, including the standardized description of MRS hardware, data acquisition, analysis, and quality assessment. This consensus statement describes each of these requirements in detail and includes a checklist to assist authors and journal reviewers and to provide a practical way for journal editors to ensure that MRS studies are reported in full.
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Affiliation(s)
- Alexander Lin
- Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Ovidiu Andronesi
- Department of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
| | - In‐Young Choi
- Department of Neurology, Hoglund Biomedical Imaging CenterUniversity of Kansas Medical CenterKansas CityKansasUSA
| | - Eduardo Coello
- Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Cristina Cudalbu
- Center for Biomedical Imaging (CIBM), Ecole Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Christoph Juchem
- Departments of Biomedical Engineering and RadiologyColumbia UniversityNew YorkNew YorkUSA
| | - Graham J. Kemp
- Department of Musculoskeletal and Ageing Science and Liverpool Magnetic Resonance Imaging Centre (LiMRIC)University of LiverpoolLiverpoolUK
| | - Roland Kreis
- Departments of Radiology and Biomedical ResearchUniversity of BernBernSwitzerland
| | - Martin Krššák
- Department of Medicine III and Department of Biomedical Imaging and Image guided TherapyMedical University of ViennaViennaAustria
| | - Phil Lee
- Department of Radiology, Hoglund Biomedical Imaging CenterUniversity of Kansas Medical CenterKansas CityKansasUSA
| | | | - Martin Meyerspeer
- High Field MR Center, Center for Medical Physics and Biomedical EngineeringMedical University of ViennaViennaAustria
| | - Vladamir Mlynarik
- Magnetic Resonance Centre of Excellence. Medical University of ViennaViennaAustria
| | - Jamie Near
- Brain Imaging Centre, Douglas Research Centre, Department of PsychiatryMcGill UniversityMontrealQuebecCanada
| | - Gülin Öz
- Center for Magnetic Resonance Research, Department of RadiologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Aimie L. Peek
- Faculty of Health SciencesUniversity of SydneySydneyAustralia
| | - Nicolaas A. Puts
- Department of Forensic and Neurodevelopmental SciencesSackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College LondonLondonUK
| | - Eva‐Maria Ratai
- A.A. Martinos Center for Biomedical Imaging, Neuroradiology Division, Department of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Ivan Tkáč
- Faculty of Health SciencesUniversity of SydneySydneyAustralia
| | - Paul G. Mullins
- Bangor Imaging Unit, School of PsychologyBangor UniversityBangorGwyneddUK
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Kantorová E, Hnilicová P, Bogner W, Grendár M, Čierny D, Hečková E, Strasser B, Ružinák R, Zeleňák K, Kurča E. Positivity of oligoclonal bands in the cerebrospinal fluid predisposed to metabolic changes and rearrangement of inhibitory/excitatory neurotransmitters in subcortical brain structures in multiple sclerosis. Mult Scler Relat Disord 2021; 52:102978. [PMID: 34015640 DOI: 10.1016/j.msard.2021.102978] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/16/2021] [Accepted: 04/18/2021] [Indexed: 12/31/2022]
Abstract
BACKGROUND The latest diagnostic criteria for multiple sclerosis (MS) have revitalized the role of oligoclonal bands synthesis in the cerebrospinal fluid (CSF-OCB). This study identifies predictors of CSF-OCB-positivity among in vivo metabolic markers in the subcortical gray/white matter in MS patients after their first episode (CIS) and in patients with relapsing-remitting course (RRMS). METHODS The study enrolled 13 CIS and 23 RRMS patients. Metabolism was evaluated using Mescher-Garwood-edited proton-magnetic resonance spectroscopy on a 3T MR scanner. In addition to N-acetyl-aspartate (tNAA), myoinositol (mIns), and choline- and creatine compounds (tCho, tCr) were also evaluated γ-aminobutyric acid (GABA) and glutamate-glutamine (Glx) ratios. RESULTS CSF-OCB-positivity was found in 76.9% of CIS and 78.2% of RRMS patients. GABA and Glx ratios in putamen and corpus callosum strongly determined CSF-OCB-positive CIS patients. Other essential predictors of CSF-OCB-positive CIS were mIns and Glx ratios in the putamen, and tCho/tNAA in the corpus callosum. In RRMS, GABA ratios in the right thalamus and Glx ratios in the left hippocampus strongly predicted CSF-OCB-positive patients. tCho/tNAA and tNAA/tCr in the left hippocampus were also identified as essential predictors of CSF-OCB-positive RRMS patients. CONCLUSION This is the first in vivo evidence of GABA-Glx rearrangement in CSF-OCB-positive patients since its early stages of MS.
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Affiliation(s)
- Ema Kantorová
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia.
| | - Petra Hnilicová
- Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia.
| | - Wolfgang Bogner
- Department of Biomedical Imaging and Image-Guided Therapy, High-field MR Center, Medical University of Vienna, 1090 Vienna, Austria.
| | - Marián Grendár
- Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia.
| | - Daniel Čierny
- Department of Clinical Biochemistry, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia.
| | - Eva Hečková
- Department of Biomedical Imaging and Image-Guided Therapy, High-field MR Center, Medical University of Vienna, 1090 Vienna, Austria.
| | - Bernhard Strasser
- Department of Biomedical Imaging and Image-Guided Therapy, High-field MR Center, Medical University of Vienna, 1090 Vienna, Austria.
| | - Róbert Ružinák
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia.
| | - Kamil Zeleňák
- Clinic of Radiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia.
| | - Egon Kurča
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia.
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Esmaeili M, Strasser B, Bogner W, Moser P, Wang Z, Andronesi OC. Whole-Slab 3D MR Spectroscopic Imaging of the Human Brain With Spiral-Out-In Sampling at 7T. J Magn Reson Imaging 2021; 53:1237-1250. [PMID: 33179836 PMCID: PMC8717862 DOI: 10.1002/jmri.27437] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 10/26/2020] [Accepted: 10/28/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Metabolic imaging using proton magnetic resonance spectroscopic imaging (MRSI) has increased the sensitivity and spectral resolution at field strengths of ≥7T. Compared to the conventional Cartesian-based spectroscopic imaging, spiral trajectories enable faster data collection, promising the clinical translation of whole-brain MRSI. Technical considerations at 7T, however, lead to a suboptimal sampling efficiency for the spiral-out (SO) acquisitions, as a significant portion of the trajectory consists of rewinders. PURPOSE To develop and implement a spiral-out-in (SOI) trajectory for sampling of whole-brain MRSI at 7T. We hypothesized that SOI will improve the signal-to-noise ratio (SNR) of metabolite maps due to a more efficient acquisition. STUDY TYPE Prospective. SUBJECTS/PHANTOM Five healthy volunteers (28-38 years, three females) and a phantom. FIELD STRENGTH/SEQUENCE Navigated adiabatic spin-echo spiral 3D MRSI at 7T. ASSESSMENT A 3D stack of SOI trajectories was incorporated into an adiabatic spin-echo MRSI sequence with real-time motion and shim correction. Metabolite spectral fitting, SNR, and Cramér-Rao lower bound (CRLB) were obtained. We compared the signal intensity and CRLB of three metabolites of tNAA, tCr, and tCho. Peak SNR (PSNR), structure similarity index (SSIM), and signal-to-artifact ratio were evaluated on water maps. STATISTICAL TESTS The nonparametric Mann-Whitney U-test was used for statistical testing. RESULTS Compared to SO, the SOI trajectory: 1) increased the k-space sampling efficiency by 23%; 2) is less demanding for the gradient hardware, requiring 36% lower Gmax and 26% lower Smax ; 3) increased PSNR of water maps by 4.94 dB (P = 0.0006); 4) resulted in a 29% higher SNR (P = 0.003) and lower CRLB by 26-35% (P = 0.02, tNAA), 35-55% (P = 0.03, tCr), and 22-23% (P = 0.04, tCho), which increased the number of well-fitted voxels (eg, for tCr by 11%, P = 0.03). SOI did not significantly change the signal-to-artifact ratio and SSIM (P = 0.65) compared to SO. DATA CONCLUSION SOI provided more efficient MRSI at 7T compared to SO, which improved the data quality and metabolite quantification. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Morteza Esmaeili
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Diagnostic Imaging, Akershus University Hospital, Lørenskog, Norway
| | - Bernhard Strasser
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Philipp Moser
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Zhe Wang
- Siemens Medical Solutions, Charlestown, Massachusetts, USA
| | - Ovidiu C. Andronesi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Poblador Rodriguez E, Moser P, Auno S, Eckstein K, Dymerska B, van der Kouwe A, Gruber S, Trattnig S, Bogner W. Real-time motion and retrospective coil sensitivity correction for CEST using volumetric navigators (vNavs) at 7T. Magn Reson Med 2021; 85:1909-1923. [PMID: 33165952 PMCID: PMC7839562 DOI: 10.1002/mrm.28555] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 09/22/2020] [Accepted: 09/22/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE To explore the impact of temporal motion-induced coil sensitivity changes on CEST-MRI at 7T and its correction using interleaved volumetric EPI navigators, which are applied for real-time motion correction. METHODS Five healthy volunteers were scanned via CEST. A 4-fold correction pipeline allowed the mitigation of (1) motion, (2) motion-induced coil sensitivity variations, ΔB1- , (3) motion-induced static magnetic field inhomogeneities, ΔB0 , and (4) spatially varying transmit RF field fluctuations, ΔB1+ . Four CEST measurements were performed per session. For the first 2, motion correction was turned OFF and then ON in absence of voluntary motion, whereas in the other 2 controlled head rotations were performed. During post-processing ΔB1- was removed additionally for the motion-corrected cases, resulting in a total of 6 scenarios to be compared. In all cases, retrospective ∆B0 and - ΔB1+ corrections were performed to compute artifact-free magnetization transfer ratio maps with asymmetric analysis (MTRasym ). RESULTS Dynamic ΔB1- correction successfully mitigated signal deviations caused by head motion. In 2 frontal lobe regions of volunteer 4, induced relative signal errors of 10.9% and 3.9% were reduced to 1.1% and 1.0% after correction. In the right frontal lobe, the motion-corrected MTRasym contrast deviated 0.92%, 1.21%, and 2.97% relative to the static case for Δω = 1, 2, 3 ± 0.25 ppm. The additional application of ΔB1- correction reduced these deviations to 0.10%, 0.14%, and 0.42%. The fully corrected MTRasym values were highly consistent between measurements with and without intended head rotations. CONCLUSION Temporal ΔB1- cause significant CEST quantification bias. The presented correction pipeline including the proposed retrospective ΔB1- correction significantly reduced motion-related artifacts on CEST-MRI.
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Affiliation(s)
- Esau Poblador Rodriguez
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - Philipp Moser
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - Sami Auno
- Neuroscience Center, University of Helsinki, Helsinki, Finland
| | - Korbinian Eckstein
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - Barbara Dymerska
- Medical Physics and Bioengineering, University College London, London, United Kingdom
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Stephan Gruber
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - Siegfried Trattnig
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.,Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
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Hangel G, Cadrien C, Lazen P, Sharma S, Furtner J, Lipka A, Heckova E, Hingerl L, Motyka S, Gruber S, Strasser B, Kiesel B, Mischkulnig M, Preusser M, Roetzer T, Wöhrer A, Widhalm G, Rössler K, Trattnig S, Bogner W. BIMG-04. MAPPING HETEROGENEITY OF HIGH-GRADE GLIOMA METABOLISM USING HIGH RESOLUTION 7T MRSI. Neurooncol Adv 2021. [PMCID: PMC7992249 DOI: 10.1093/noajnl/vdab024.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVES Neurosurgical resection in gliomas depends on the precise preoperative definition of the tumor and its margins to realize a safe maximum resection that translates into a better patient outcome. New metabolic imaging techniques could improve this delineation as well as designate targets for biopsies. We validated the performance of our fast high-resolution whole-brain 3D-magnetic resonance spectroscopic imaging (MRSI) method at 7T in high-grade gliomas (HGGs) as first step to this regard. METHODS We measured 23 patients with HGGs at 7T with MRSI covering the whole cerebrum with 3.4mm isotropic resolution in 15 min. Quantification used a basis-set of 17 neurochemical components. They were evaluated for their reliability/quality and compared to neuroradiologically segmented tumor regions-of-interest (necrosis, contrast-enhanced, non-contrast-enhanced+edema, peritumoral) and histopathology (e.g., grade, IDH-status). RESULTS We found 18/23 measurements to be usable and ten neurochemicals quantified with acceptable quality. The most common denominators were increases of glutamine, glycine, and total choline as well as decreases of N-acetyl-aspartate and total creatine over most tumor regions. Other metabolites like taurine and serine showed mixed behavior. We further found that heterogeneity in the metabolic images often continued into the peritumoral region. While 2-hydroxy-glutarate could not be satisfyingly quantified, we found a tendency for a decrease of glutamate in IDH1-mutant HGGs. DISCUSSION Our findings corresponded well to clinical tumor segmentation but were more heterogeneous and often extended into the peritumoral region. Our results corresponded to previous knowledge, but with previously not feasible resolution. Apart from glycine/glutamine and their role in glioma progression, more research on the connection of glutamate and others to specific mutations is necessary. The addition of low-grade gliomas and statistical ROI analysis in a larger cohort will be the next important steps to define the benefits of our 7T MRSI approach for the definition of spatial metabolic tumor profiles.
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Affiliation(s)
| | | | | | | | | | | | - Eva Heckova
- Medical University of Vienna, Vienna, Austria
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Bednarik P, Spurny B, Silberbauer LR, Svatkova A, Handschuh PA, Reiter B, Konadu ME, Stimpfl T, Spies M, Bogner W, Lanzenberger R. Effect of Ketamine on Human Neurochemistry in Posterior Cingulate Cortex: A Pilot Magnetic Resonance Spectroscopy Study at 3 Tesla. Front Neurosci 2021; 15:609485. [PMID: 33841073 PMCID: PMC8024494 DOI: 10.3389/fnins.2021.609485] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 02/23/2021] [Indexed: 12/28/2022] Open
Abstract
Ketamine is a powerful glutamatergic long-lasting antidepressant, efficient in intractable major depression. Whereas ketamine's immediate psychomimetic side-effects were linked to glutamate changes, proton MRS (1H-MRS) showed an association between the ratio of glutamate and glutamine and delayed antidepressant effect emerging ∼2 h after ketamine administration. While most 1H-MRS studies focused on anterior cingulate, recent functional MRI connectivity studies revealed an association between ketamine's antidepressant effect and disturbed connectivity patterns to the posterior cingulate cortex (PCC), and related PCC dysfunction to rumination and memory impairment involved in depressive pathophysiology. The current study utilized the state-of-the-art single-voxel 3T sLASER 1H-MRS methodology optimized for reproducible measurements. Ketamine's effects on neurochemicals were assessed before and ∼3 h after intravenous ketamine challenge in PCC. Concentrations of 11 neurochemicals, including glutamate (CRLB ∼ 4%) and glutamine (CRLB ∼ 13%), were reliably quantified with the LCModel in 12 healthy young men with between-session coefficients of variation (SD/mean) <8%. Also, ratios of glutamate/glutamine and glutamate/aspartate were assessed as markers of synaptic function and activated glucose metabolism, respectively. Pairwise comparison of metabolite profiles at baseline and 193 ± 4 min after ketamine challenge yielded no differences. Minimal detectable concentration differences estimated with post hoc power analysis (power = 80%, alpha = 0.05) were below 0.5 μmol/g, namely 0.39 μmol/g (∼4%) for glutamate, 0.28 μmol/g (∼10%) for Gln, ∼14% for glutamate/glutamine and ∼8% for glutamate/aspartate. Despite the high sensitivity to detect between-session differences in glutamate and glutamine concentrations, our study did not detect delayed glutamatergic responses to subanesthetic ketamine doses in PCC.
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Affiliation(s)
- Petr Bednarik
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Institute for Clinical Molecular MRI in Musculoskeletal System, Karl Landsteiner Society, Vienna, Austria
| | - Benjamin Spurny
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Leo R. Silberbauer
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Alena Svatkova
- Department of Medicine III, Clinical Division of Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria
| | - Patricia A. Handschuh
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Birgit Reiter
- Clinical Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Melisande E. Konadu
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Thomas Stimpfl
- Clinical Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Marie Spies
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Institute for Clinical Molecular MRI in Musculoskeletal System, Karl Landsteiner Society, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
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Imrich R, Vlcek M, Penesova A, Radikova Z, Havranova A, Sivakova M, Siarnik P, Kollar B, Sokolov T, Turcani P, Heckova E, Hangel G, Strasser B, Bogner W. Cardiac autonomic function in patients with early multiple sclerosis. Clin Auton Res 2021; 31:553-562. [PMID: 33665745 DOI: 10.1007/s10286-021-00790-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/19/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE Cardiac autonomic dysfunction has been reported in patients with long-standing multiple sclerosis (MS); however, data in early disease are limited. The present study was aimed at evaluating cardiac autonomic function in patients with early MS in the context of white matter metabolic status, which could potentially affect functions of the autonomic brain centers. METHODS Cardiac sympathetic and baroreflex cardiovagal responses to the Valsalva maneuver, orthostatic test, and the Stroop test were evaluated in 16 early, treatment-naïve patients with relapsing-remitting MS, and in 14 healthy participants. Proton magnetic resonance spectroscopic imaging (MRSI) of the brain was performed in eight of these MS patients and in eight controls. RESULTS Valsalva maneuver outcomes were comparable between patients and controls. At baseline, norepinephrine levels were lower (p = 0.027) in MS patients compared to controls. The patients had higher heart rate (p = 0.034) and lower stroke volume (p = 0.008), but similar blood pressure, cardiac output and norepinephrine increments from baseline to 2 min of the orthostatic test compared to controls. MS patients and controls did not differ in responses to the Stroop test. MRSI showed lower total N-acetylaspartate/total creatine (p = 0.038) and higher myo-inositol/total creatine (p = 0.013) in MS lesions compared to non-lesional white matter. CONCLUSION Our results show normal cardiac sympathetic and baroreflex cardiovagal function in MS patients with relapsing-remitting MS with lesions at the post-acute/early resolving stage. TRIAL REGISTRATION The study was registered at ClinicalTrials.gov under the Identifier: NCT03052595 and complies with the STROBE checklist for cohort, case-control, and cross-sectional studies.
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Affiliation(s)
- Richard Imrich
- Institute of Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, Dúbravská cesta 9, 84505, Bratislava, Slovakia.
| | - Miroslav Vlcek
- Institute of Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, Dúbravská cesta 9, 84505, Bratislava, Slovakia.,Faculty of Medicine, Slovak Medical University in Bratislava, Bratislava, Slovakia
| | - Adela Penesova
- Institute of Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, Dúbravská cesta 9, 84505, Bratislava, Slovakia
| | - Zofia Radikova
- Institute of Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, Dúbravská cesta 9, 84505, Bratislava, Slovakia
| | - Andrea Havranova
- Institute of Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, Dúbravská cesta 9, 84505, Bratislava, Slovakia
| | - Monika Sivakova
- 1St Department of Neurology, Faculty of Medicine, Comenius University, Bratislava, Slovakia
| | - Pavel Siarnik
- 1St Department of Neurology, Faculty of Medicine, Comenius University, Bratislava, Slovakia
| | - Branislav Kollar
- 1St Department of Neurology, Faculty of Medicine, Comenius University, Bratislava, Slovakia
| | | | - Peter Turcani
- 1St Department of Neurology, Faculty of Medicine, Comenius University, Bratislava, Slovakia
| | - Eva Heckova
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Gilbert Hangel
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Bernhard Strasser
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
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Bachrata B, Strasser B, Bogner W, Schmid AI, Korinek R, Krššák M, Trattnig S, Robinson SD. Simultaneous Multiple Resonance Frequency imaging (SMURF): Fat-water imaging using multi-band principles. Magn Reson Med 2021; 85:1379-1396. [PMID: 32981114 PMCID: PMC7756227 DOI: 10.1002/mrm.28519] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 07/31/2020] [Accepted: 08/24/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE To develop a fat-water imaging method that allows reliable separation of the two tissues, uses established robust reconstruction methods, and requires only one single-echo acquisition. THEORY AND METHODS The proposed method uses spectrally selective dual-band excitation in combination with CAIPIRINHA to generate separate images of fat and water simultaneously. Spatially selective excitation without cross-contamination is made possible by the use of spatial-spectral pulses. Fat and water images can either be visualized separately, or the fat images can be corrected for chemical shift displacement and, in gradient echo imaging, for chemical shift-related phase discrepancy, and recombined with water images, generating fat-water images free of chemical shift effects. Gradient echo and turbo spin echo sequences were developed based on this Simultaneous Multiple Resonance Frequency imaging (SMURF) approach and their performance was assessed at 3Tesla in imaging of the knee, breasts, and abdomen. RESULTS The proposed method generated well-separated fat and water images with minimal unaliasing artefacts or cross-excitation, evidenced by the near absence of water signal attributed to the fat image and vice versa. The separation achieved was similar to or better than that using separate acquisitions with water- and fat-saturation or Dixon methods. The recombined fat-water images provided similar image contrast to conventional images, but the chemical shift effects were eliminated. CONCLUSION Simultaneous Multiple Resonance Frequency imaging is a robust fat-water imaging technique that offers a solution to imaging of body regions with significant amounts of fat.
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Affiliation(s)
- Beata Bachrata
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria,Christian Doppler Laboratory for Clinical Molecular MR ImagingViennaAustria
| | - Bernhard Strasser
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria,Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMAUSA
| | - Wolfgang Bogner
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Albrecht Ingo Schmid
- High Field MR Centre, Center for Medical Physics and Biomedical EngineeringMedical University of ViennaViennaAustria
| | - Radim Korinek
- Institute of Scientific Instruments of the CASBrnoCzech Republic
| | - Martin Krššák
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria,Christian Doppler Laboratory for Clinical Molecular MR ImagingViennaAustria,Department of Internal Medicine III, Division of Endocrinology and MetabolismMedical University of ViennaViennaAustria
| | - Siegfried Trattnig
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria,Christian Doppler Laboratory for Clinical Molecular MR ImagingViennaAustria
| | - Simon Daniel Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria,Centre of Advanced ImagingUniversity of QueenslandBrisbaneQLDAustralia,Department of NeurologyMedical University of GrazGrazAustria
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Dorst J, Ruhm L, Avdievich N, Bogner W, Henning A. Comparison of four 31P single-voxel MRS sequences in the human brain at 9.4 T. Magn Reson Med 2021; 85:3010-3026. [PMID: 33427322 DOI: 10.1002/mrm.28658] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 12/03/2020] [Accepted: 12/06/2020] [Indexed: 01/30/2023]
Abstract
PURPOSE In this study, different single-voxel localization sequences were implemented and systematically compared for the first time for phosphorous MRS (31 P-MRS) in the human brain at 9.4 T. METHODS Two multishot sequences, image-selected in vivo spectroscopy (ISIS) and a conventional slice-selective excitation combined with localization by adiabatic selective refocusing (semiLASER) variant of the spin-echo full intensity-acquired localized spectroscopy (SPECIAL-semiLASER), and two single-shot sequences, semiLASER and stimulated echo acquisition mode (STEAM), were implemented and optimized for 31 P-MRS in the human brain at 9.4 T. Pulses and coil setup were optimized, localization accuracy was tested in phantom experiments, and absolute SNR of the sequences was compared in vivo. The SNR per unit time (SNR/t) was derived and compared for all four sequences and verified experimentally for ISIS in two different voxel sizes (3 × 3 × 3 cm3 , 5 × 5 × 5 cm3 , 10-minute measurement time). Metabolite signals obtained with ISIS were quantified. The possible spectral quality in vivo acquired in clinically feasible time (3:30 minutes, 3 × 3 × 3 cm3 ) was explored for two different coil setups. RESULTS All evaluated sequences performed with good localization accuracy in phantom experiments and provided well-resolved spectra in vivo. However, ISIS has the lowest chemical shift displacement error, the best localization accuracy, the highest SNR/t for most metabolites, provides metabolite concentrations comparable to literature values, and is the only one of the sequences that allows for the detection of the whole 31 P spectrum, including β-adenosine triphosphate, with the used setup. The SNR/t of STEAM is comparable to the SNR/t of ISIS. The semiLASER and SPECIAL-semiLASER sequences provide good results for metabolites with long T2 . CONCLUSION At 9.4 T, high-quality single-voxel localized 31 P-MRS can be performed in the human brain with different localization methods, each with inherent characteristics suitable for different research issues.
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Affiliation(s)
- Johanna Dorst
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany
| | - Loreen Ruhm
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany
| | - Nikolai Avdievich
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Wolfgang Bogner
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Anke Henning
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA
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Andronesi OC, Nicholson K, Jafari-Khouzani K, Bogner W, Wang J, Chan J, Macklin EA, Levine-Weinberg M, Breen C, Schwarzschild MA, Cudkowicz M, Rosen BR, Paganoni S, Ratai EM. Imaging Neurochemistry and Brain Structure Tracks Clinical Decline and Mechanisms of ALS in Patients. Front Neurol 2020; 11:590573. [PMID: 33343494 PMCID: PMC7744722 DOI: 10.3389/fneur.2020.590573] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 11/03/2020] [Indexed: 12/27/2022] Open
Abstract
Background: Oxidative stress and protein aggregation are key mechanisms in amyotrophic lateral sclerosis (ALS) disease. Reduced glutathione (GSH) is the most important intracellular antioxidant that protects neurons from reactive oxygen species. We hypothesized that levels of GSH measured by MR spectroscopic imaging (MRSI) in the motor cortex and corticospinal tract are linked to clinical trajectory of ALS patients. Objectives: Investigate the value of GSH imaging to probe clinical decline of ALS patients in combination with other neurochemical and structural parameters. Methods: Twenty-four ALS patients were imaged at 3 T with an advanced MR protocol. Mapping GSH levels in the brain is challenging, and for this purpose, we used an optimized spectral-edited 3D MRSI sequence with real-time motion and field correction to image glutathione and other brain metabolites. In addition, our imaging protocol included (i) an adiabatic T1ρ sequence to image macromolecular fraction of brain parenchyma, (ii) diffusion tensor imaging (DTI) for white matter tractography, and (iii) high-resolution anatomical imaging. Results: We found GSH in motor cortex (r = −0.431, p = 0.04) and corticospinal tract (r = −0.497, p = 0.016) inversely correlated with time between diagnosis and imaging. N-Acetyl-aspartate (NAA) in motor cortex inversely correlated (r = −0.416, p = 0.049), while mean water diffusivity (r = 0.437, p = 0.033) and T1ρ (r = 0.482, p = 0.019) positively correlated with disease progression measured by imputed change in revised ALS Functional Rating Scale. There is more decrease in the motor cortex than in the white matter for GSH compared to NAA, glutamate, and glutamine. Conclusions: Our study suggests that a panel of biochemical and structural imaging biomarkers defines a brain endophenotype, which can be used to time biological events and clinical progression in ALS patients. Such a quantitative brain endophenotype may stratify ALS patients into more homogeneous groups for therapeutic interventions compared to clinical criteria.
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Affiliation(s)
- Ovidiu C Andronesi
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States
| | - Katharine Nicholson
- Neurological Clinical Research Institute (NCRI), Massachusetts General Hospital, Boston, MA, United States
| | - Kourosh Jafari-Khouzani
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States
| | - Wolfgang Bogner
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Jing Wang
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States.,Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - James Chan
- Biostatistics Center, Massachusetts General Hospital, Boston, MA, United States
| | - Eric A Macklin
- Biostatistics Center, Massachusetts General Hospital, Boston, MA, United States
| | - Mark Levine-Weinberg
- Neurological Clinical Research Institute (NCRI), Massachusetts General Hospital, Boston, MA, United States
| | - Christopher Breen
- Neurological Clinical Research Institute (NCRI), Massachusetts General Hospital, Boston, MA, United States
| | | | - Merit Cudkowicz
- Neurological Clinical Research Institute (NCRI), Massachusetts General Hospital, Boston, MA, United States
| | - Bruce R Rosen
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States
| | - Sabrina Paganoni
- Neurological Clinical Research Institute (NCRI), Massachusetts General Hospital, Boston, MA, United States.,Spaulding Rehabilitation Hospital, Boston, MA, United States
| | - Eva-Maria Ratai
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States
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50
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Hangel G, Cadrien C, Lazen P, Furtner J, Lipka A, Hečková E, Hingerl L, Motyka S, Gruber S, Strasser B, Kiesel B, Mischkulnig M, Preusser M, Roetzer T, Wöhrer A, Widhalm G, Rössler K, Trattnig S, Bogner W. High-resolution metabolic imaging of high-grade gliomas using 7T-CRT-FID-MRSI. Neuroimage Clin 2020; 28:102433. [PMID: 32977210 PMCID: PMC7511769 DOI: 10.1016/j.nicl.2020.102433] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 09/08/2020] [Accepted: 09/09/2020] [Indexed: 12/21/2022]
Abstract
OBJECTIVES Successful neurosurgical intervention in gliomas depends on the precision of the preoperative definition of the tumor and its margins since a safe maximum resection translates into a better patient outcome. Metabolic high-resolution imaging might result in improved presurgical tumor characterization, and thus optimized glioma resection. To this end, we validated the performance of a fast high-resolution whole-brain 3D-magnetic resonance spectroscopic imaging (MRSI) method at 7T in a patient cohort of 23 high-grade gliomas (HGG). MATERIALS AND METHODS We preoperatively measured 23 patients with histologically verified HGGs (17 male, 8 female, age 53 ± 15) with an MRSI sequence based on concentric ring trajectories with a 64 × 64 × 39 measurement matrix, and a 3.4 × 3.4 × 3.4 mm3 nominal voxel volume in 15 min. Quantification used a basis-set of 17 components including N-acetyl-aspartate (NAA), total choline (tCho), total creatine (tCr), glutamate (Glu), glutamine (Gln), glycine (Gly) and 2-hydroxyglutarate (2HG). The resultant metabolic images were evaluated for their reliability as well as their quality and compared to spatially segmented tumor regions-of-interest (necrosis, contrast-enhanced, non-contrast enhanced + edema, peritumoral) based on clinical data and also compared to histopathology (e.g., grade, IDH-status). RESULTS Eighteen of the patient measurements were considered usable. In these patients, ten metabolites were quantified with acceptable quality. Gln, Gly, and tCho were increased and NAA and tCr decreased in nearly all tumor regions, with other metabolites such as serine, showing mixed trends. Overall, there was a reliable characterization of metabolic tumor areas. We also found heterogeneity in the metabolic images often continued into the peritumoral region. While 2HG could not be satisfyingly quantified, we found an increase of Glu in the contrast-enhancing region of IDH-wildtype HGGs and a decrease of Glu in IDH1-mutant HGGs. CONCLUSIONS We successfully demonstrated high-resolution 7T 3D-MRSI in HGG patients, showing metabolic differences between tumor regions and peritumoral tissue for multiple metabolites. Increases of tCho, Gln (related to tumor metabolism), Gly (related to tumor proliferation), as well as decreases in NAA, tCr, and others, corresponded very well to clinical tumor segmentation, but were more heterogeneous and often extended into the peritumoral region.
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Affiliation(s)
- Gilbert Hangel
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Department of Neurosurgery, Medical University of Vienna, Vienna, Austria.
| | - Cornelius Cadrien
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Philipp Lazen
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Julia Furtner
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Alexandra Lipka
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
| | - Eva Hečková
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lukas Hingerl
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Stanislav Motyka
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Stephan Gruber
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Bernhard Strasser
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Barbara Kiesel
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Mario Mischkulnig
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Matthias Preusser
- Division of Oncology, Department of Inner Medicine I, Medical University of Vienna, Vienna, Austria
| | - Thomas Roetzer
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Adelheid Wöhrer
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Georg Widhalm
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Karl Rössler
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Siegfried Trattnig
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
| | - Wolfgang Bogner
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
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