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Dell'Orco A, Riemann LT, Ellison SLR, Aydin S, Göschel L, Tietze A, Scheel M, Fillmer A. Macromolecule modelling for improved metabolite quantification using short echo time brain 1 H MRS at 3 T and 7 T: The PRaMM Model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.16.567383. [PMID: 38014000 PMCID: PMC10680753 DOI: 10.1101/2023.11.16.567383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Purpose To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain 1 H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Methods Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared to those other methods was investigated. Results The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. While the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p≤0.0001). Minimally detectable changes are in the range 0.5 - 1.9 mM and percent coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out. Conclusion Here, the PRaMM model, a method for an improved quantification of metabolites was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.
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Gudmundson AT, Davies-Jenkins CW, Özdemir İ, Murali-Manohar S, Zöllner HJ, Song Y, Hupfeld KE, Schnitzler A, Oeltzschner G, Stark CEL, Edden RAE. Application of a 1H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.08.539813. [PMID: 37215030 PMCID: PMC10197548 DOI: 10.1101/2023.05.08.539813] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and prediction of OOV signals. AGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log10 normed-MSE of -1.79, an improvement of almost two orders of magnitude.
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Affiliation(s)
- Aaron T Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Christopher W Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - İpek Özdemir
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Saipavitra Murali-Manohar
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Helge J Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Kathleen E Hupfeld
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Craig E L Stark
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
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3
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Harris AD, Amiri H, Bento M, Cohen R, Ching CRK, Cudalbu C, Dennis EL, Doose A, Ehrlich S, Kirov II, Mekle R, Oeltzschner G, Porges E, Souza R, Tam FI, Taylor B, Thompson PM, Quidé Y, Wilde EA, Williamson J, Lin AP, Bartnik-Olson B. Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations. Front Neurol 2023; 13:1045678. [PMID: 36686533 PMCID: PMC9845632 DOI: 10.3389/fneur.2022.1045678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/06/2022] [Indexed: 01/06/2023] Open
Abstract
Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impact, however, has been limited due to small sample sizes and methodological variability in addition to intrinsic limitations of the method itself such as its sensitivity to motion. The lack of standardization from a data acquisition and data processing perspective makes it difficult to pool multiple studies and/or conduct multisite studies that are necessary for supporting clinically relevant findings. Based on the experience of the ENIGMA MRS work group and a review of the literature, this manuscript provides an overview of the current state of MRS data harmonization. Key factors that need to be taken into consideration when conducting both retrospective and prospective studies are described. These include (1) MRS acquisition issues such as pulse sequence, RF and B0 calibrations, echo time, and SNR; (2) data processing issues such as pre-processing steps, modeling, and quantitation; and (3) biological factors such as voxel location, age, sex, and pathology. Various approaches to MRS data harmonization are then described including meta-analysis, mega-analysis, linear modeling, ComBat and artificial intelligence approaches. The goal is to provide both novice and experienced readers with the necessary knowledge for conducting MRS data harmonization studies.
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Affiliation(s)
- Ashley D. Harris
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Houshang Amiri
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
| | - Mariana Bento
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Ronald Cohen
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, Los Angeles, CA, United States
| | - Christina Cudalbu
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Emily L. Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States
| | - Arne Doose
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Ivan I. Kirov
- Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY, United States
| | - Ralf Mekle
- Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Eric Porges
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Roberto Souza
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Friederike I. Tam
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Brian Taylor
- Division of Diagnostic Imaging, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, Los Angeles, CA, United States
| | - Yann Quidé
- School of Psychology, University of New South Wales (UNSW), Sydney, NSW, Australia
| | - Elisabeth A. Wilde
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States
| | - John Williamson
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Alexander P. Lin
- Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Brenda Bartnik-Olson
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, United States
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4
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Ziegs T, Dorst J, Ruhm L, Avdievitch N, Henning A. Measurement of glucose metabolism in the occipital lobe and frontal cortex after oral administration of [1-13C]glucose at 9.4 T. J Cereb Blood Flow Metab 2022; 42:1890-1904. [PMID: 35632989 PMCID: PMC9536126 DOI: 10.1177/0271678x221104540] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 04/29/2022] [Accepted: 05/05/2022] [Indexed: 12/02/2022]
Abstract
For the first time, labeling effects after oral intake of [1-13C]glucose are observed in the human brain with pure 1H detection at 9.4 T. Spectral time series were acquired using a short-TE 1H MRS MC-semiLASER (Metabolite Cycling semi Localization by Adiabatic SElective Refocusing) sequence in two voxels of 5.4 mL in the frontal cortex and the occipital lobe. High-quality time-courses of [4-13C]glutamate, [4-13C]glutamine, [3-13C]glutamate + glutamine, [2-13C] glutamate+glutamine and [3-13C]aspartate for individual volunteers and additionally, group-averaged time-courses of labeled and non-labeled brain glucose could be obtained. Using a one-compartment model, mean metabolic rates were calculated for each voxel position: The mean rate of the TCA-cycle (Vtca) value was determined to be 1.36 and 0.93 μmol min-1 g-1, the mean rate of glutamine synthesis (Vgln) was calculated to be 0.23 and 0.45 μmol min-1 g-1, the mean exchange rate between cytosolic amino acids and mitochondrial Krebs cycle intermediates (Vx) rate was found to be 0.57 and 1.21 μmol min-1 g-1 for the occipital lobe and the frontal cortex, respectively. These values were in agreement with previously reported data. Altogether, it can be shown that this most simple technique combining oral administration of [1-13C]Glc with pure 1H MRS acquisition is suitable to measure metabolic rates.
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Affiliation(s)
- Theresia Ziegs
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Johanna Dorst
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Loreen Ruhm
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Nikolai Avdievitch
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Anke Henning
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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5
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Ziegs T, Wright AM, Henning A. Test-retest reproducibility of human brain multi-slice 1 H FID-MRSI data at 9.4T after optimization of lipid regularization, macromolecular model, and spline baseline stiffness. Magn Reson Med 2022; 89:11-28. [PMID: 36128885 DOI: 10.1002/mrm.29423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/28/2022] [Accepted: 08/02/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE This study analyzes the effects of retrospective lipid suppression, a simulated macromolecular prior knowledge and different spline baseline stiffness values on 9.4T multi-slice proton FID-MRSI data spanning the whole cerebrum of human brain and the reproducibility of respective metabolite ratio to total creatine (/tCr) maps for 10 brain metabolites. METHODS Measurements were performed twice on 5 volunteers using a short TR and TE FID MRSI 2D sequence at 9.4T. The effects of retrospective lipid L2-regularization, macromolecular spectrum and different LCModel baseline flexibilities on SNR, FWHM, fitting residual, Cramér-Rao lower bound, and metabolite ratio maps were investigated. Intra-subject, inter-session coefficient of variation and the test-retest reproducibility of the mean metabolite ratios (/tCr) of each slice was calculated. RESULTS Transversal, sagittal, and coronal slices of many metabolite ratio maps correspond to the anatomically expected concentration relations in gray and white matter for the majority of the cerebrum when using a flexible baseline in LCModel fit. Results from the second measurements of the same subjects show that slice positioning and data quality correlate significantly to the first measurement. L2-regularization provided effective suppression of lipid-artifacts, but should be avoided if no artifacts are detected. CONCLUSION Reproducible concentration ratio maps (/tCr) for 4 metabolites (total choline, N-acetylaspartate, glutamate, and myoinositol) spanning the majority of the cerebrum and 6 metabolites (N-acetylaspartylglutamate, γ-aminobutyric acid, glutathione, taurine, glutamine, and aspartate) covering 32 mm in the upper part of the brain were acquired at 9.4T using multi-slice FID MRSI with retrospective lipid suppression, a macromolecular spectrum and a flexible LCModel baseline.
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Affiliation(s)
- Theresia Ziegs
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Andrew Martin Wright
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Anke Henning
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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6
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Şimşek K, Döring A, Pampel A, Möller HE, Kreis R. Macromolecular background signal and non-Gaussian metabolite diffusion determined in human brain using ultra-high diffusion weighting. Magn Reson Med 2022; 88:1962-1977. [PMID: 35803740 PMCID: PMC9545875 DOI: 10.1002/mrm.29367] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 05/08/2022] [Accepted: 05/31/2022] [Indexed: 12/14/2022]
Abstract
Purpose Definition of a macromolecular MR spectrum based on diffusion properties rather than relaxation time differences and characterization of non‐Gaussian diffusion of brain metabolites with strongly diffusion‐weighted MR spectroscopy. Methods Short echo time MRS with strong diffusion‐weighting with b‐values up to 25 ms/μm2 at two diffusion times was implemented on a Connectom system and applied in combination with simultaneous spectral and diffusion decay modeling. Motion‐compensation was performed with a combined method based on the simultaneously acquired water and a macromolecular signal. Results The motion compensation scheme prevented spurious signal decay reflected in very small apparent diffusion constants for macromolecular signal. Macromolecular background signal patterns were determined using multiple fit strategies. Signal decay corresponding to non‐Gaussian metabolite diffusion was represented by biexponential fit models yielding parameter estimates for human gray matter that are in line with published rodent data. The optimal fit strategies used constraints for the signal decay of metabolites with limited signal contributions to the overall spectrum. Conclusion The determined macromolecular spectrum based on diffusion properties deviates from the conventional one derived from longitudinal relaxation time differences calling for further investigation before use as experimental basis spectrum when fitting clinical MR spectra. The biexponential characterization of metabolite signal decay is the basis for investigations into pathologic alterations of microstructure. Click here for author‐reader discussions
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Affiliation(s)
- Kadir Şimşek
- Magnetic Resonance MethodologyInstitute of Diagnostic and Interventional Neuroradiology, University of BernBernSwitzerland
- Graduate School for Cellular and Biomedical SciencesUniversity of BernBernSwitzerland
- Translational Imaging Center (TIC)Swiss Institute for Translational and Entrepreneurial MedicineBernSwitzerland
| | - André Döring
- Cardiff University Brain Research Imaging Centre (CUBRIC)School of Psychology, Cardiff UniversityCardiffUK
| | - André Pampel
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Harald E. Möller
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Roland Kreis
- Magnetic Resonance MethodologyInstitute of Diagnostic and Interventional Neuroradiology, University of BernBernSwitzerland
- Translational Imaging Center (TIC)Swiss Institute for Translational and Entrepreneurial MedicineBernSwitzerland
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7
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Dorst J, Borbath T, Landheer K, Avdievich N, Henning A. Simultaneous detection of metabolite concentration changes, water BOLD signal and pH changes during visual stimulation in the human brain at 9.4T. J Cereb Blood Flow Metab 2022; 42:1104-1119. [PMID: 35060409 PMCID: PMC9121534 DOI: 10.1177/0271678x221075892] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 12/14/2021] [Accepted: 12/22/2021] [Indexed: 12/15/2022]
Abstract
This study presents a method to directly link metabolite concentration changes and BOLD response in the human brain during visual stimulation by measuring the water and metabolite signals simultaneously. Therefore, the metabolite-cycling (MC) non-water suppressed semiLASER localization technique was optimized for functional 1H MRS in the human brain at 9.4 T. Data of 13 volunteers were acquired during a 26:40 min visual stimulation block-design paradigm. Activation-induced BOLD signal was observed in the MC water signal as well as in the NAA-CH3 and tCr-CH3 singlets. During stimulation, glutamate concentration increased 2.3 ± 2.0% to a new steady-state, while a continuous increase over the whole stimulation period could be observed in lactate with a mean increase of 35.6 ± 23.1%. These increases of Lac and Glu during brain activation confirm previous findings reported in literature. A positive correlation of the MC water BOLD signal with glutamate and lactate concentration changes was found. In addition, a pH decrease calculated from a change in the ratio of PCr to Cr was observed during brain activation, particularly at the onset of the stimulation.
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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, University of Tübingen, Tübingen, Germany
| | - Tamas Borbath
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Faculty of Science, University of Tübingen, University of Tübingen, Tübingen, Germany
| | | | - Nikolai Avdievich
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Anke Henning
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA
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8
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Roussel T, Le Fur Y, Guye M, Viout P, Ranjeva JP, Callot V. Respiratory-triggered quantitative MR spectroscopy of the human cervical spinal cord at 7 T. Magn Reson Med 2022; 87:2600-2612. [PMID: 35181915 DOI: 10.1002/mrm.29182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 01/07/2022] [Accepted: 01/12/2022] [Indexed: 02/06/2023]
Abstract
PURPOSE Ultra-high field 1 H MR spectroscopy (MRS) is of great interest to help characterizing human spinal cord pathologies. However, very few studies have been reported so far in this small size structure at these fields due to challenging experimental difficulties caused by static and radiofrequency field heterogeneities, as well as physiological motion. In this work, in line with the recent developments proposed to strengthen spinal cord MRS feasibility at 7 T, a respiratory-triggered acquisition approach was optimized to compensate for dynamic B 0 field heterogeneities and to provide robust cervical spinal cord MRS data. METHODS A semi-LASER sequence was purposely used, and a dedicated raw data processing algorithm was developed to enhance MR spectral quality by discarding corrupted scans. To legitimate the choices done during the optimization stage, additional tests were carried out to determine the impact of breathing, voluntary motion, body mass index, and fitting algorithm. An in-house quantification tool was concomitantly designed for accurate estimation of the metabolite concentration ratios for choline, N-acetyl-aspartate (NAA), myo-inositol and glutathione. The method was tested on a cohort of 14 healthy volunteers. RESULTS Average water linewidth and NAA signal-to-noise ratio reached 0.04 ppm and 11.01, respectively. The group-average metabolic ratios were in good agreement with previous studies and showed intersession reproducibility variations below 30%. CONCLUSION The developed approach allows a rise of the acquired MRS signal quality and of the quantification robustness as compared to previous studies hence offering strengthened possibilities to probe the metabolism of degenerative and traumatic spinal cord pathologies.
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Affiliation(s)
- Tangi Roussel
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Yann Le Fur
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Patrick Viout
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Jean-Philippe Ranjeva
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Virginie Callot
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
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9
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Tomiyasu M, Harada M. In vivo Human MR Spectroscopy Using a Clinical Scanner: Development, Applications, and Future Prospects. Magn Reson Med Sci 2022; 21:235-252. [PMID: 35173095 PMCID: PMC9199975 DOI: 10.2463/mrms.rev.2021-0085] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
MR spectroscopy (MRS) is a unique and useful method for noninvasively evaluating biochemical metabolism in human organs and tissues, but its clinical dissemination has been slow and often limited to specialized institutions or hospitals with experts in MRS technology. The number of 3-T clinical MR scanners is now increasing, representing a major opportunity to promote the use of clinical MRS. In this review, we summarize the theoretical background and basic knowledge required to understand the results obtained with MRS and introduce the general consensus on the clinical utility of proton MRS in routine clinical practice. In addition, we present updates to the consensus guidelines on proton MRS published by the members of a working committee of the Japan Society of Magnetic Resonance in Medicine in 2013. Recent research into multinuclear MRS equipped in clinical MR scanners is explained with an eye toward future development. This article seeks to provide an overview of the current status of clinical MRS and to promote the understanding of when it can be useful. In the coming years, MRS-mediated biochemical evaluation is expected to become available for even routine clinical practice.
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Affiliation(s)
- Moyoko Tomiyasu
- Department of Molecular Imaging and Theranostics, National Institutes for Quantum Science and Technology.,Department of Radiology, Kanagawa Children's Medical Center
| | - Masafumi Harada
- Department of Radiology and Radiation Oncology, Graduate School of Biomedical Sciences, Tokushima University
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10
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Fischer A, Martirosian P, Benkert T, Schick F. Spatially resolved free-induction decay spectroscopy using a 3D ultra-short echo time multi-echo imaging sequence with systematic echo shifting and compensation of B 0 field drifts. Magn Reson Med 2021; 87:2099-2110. [PMID: 34866240 DOI: 10.1002/mrm.29115] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/15/2021] [Accepted: 11/18/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE Biologically interesting signals can exhibit fast transverse relaxation and frequency shifts compared to free water. For spectral assignment, a ultra-short echo time (UTE) imaging sequence was modified to provide pixel-wise free-induction decay (FID) acquisition. METHODS The UTE-FID approach presented relies on a multi-echo 3D spiral UTE sequence with six echoes per radiofrequency (RF) excitation (TEmin 0.05 ms, echo spacing 3 ms). A complex pixel-wise raw data set for FID spectroscopy is obtained by several multi-echo UTE measurements with systematic shifting of the readout by 0.25 or 0.5 ms, until the time domain is filled for 18 or 45 ms. B0 drifts are compensated by mapping and according phase correction. Autoregressive extrapolation of the signal is performed before Gaussian filtering. This method was applied to a phantom containing collagen-water solutions of different concentrations. To calculate the collagen content, a 19-peak collagen model was extracted from a non-selective FID spectrum (50% collagen solution). Proton-density-collagen-fraction (PDCF) was calculated for 10 collagen solutions (2%-50%). Furthermore, an in vivo UTE-FID spectrum of adipose tissue was recorded. RESULTS UTE-FID signal patterns agreed well with the non-spatially selective pulse-acquire FID spectrum from a sphere filled with 50% collagen. Differentiation of collagen solution from distilled water in the PDCF map was possible from 4% collagen concentration for a UTE-FID sequence with 128 × 128 × 64 matrix (voxel size 1 × 1 × 2.85 mm3 ). The mean values of the PDCF correlate linearly with collagen concentration. CONCLUSION The presented UTE-FID approach allows pixel-wise raw data acquisition similar to non-spatially selective pulse-acquire spectroscopy. Spatially resolved applications for assessment of spectra of rapidly decaying signals seem feasible.
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Affiliation(s)
- Anja Fischer
- Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Petros Martirosian
- Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Thomas Benkert
- MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Fritz Schick
- Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany
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11
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Hui SCN, Gong T, Zöllner HJ, Song Y, Murali-Manohar S, Oeltzschner G, Mikkelsen M, Tapper S, Chen Y, Saleh MG, Porges EC, Chen W, Wang G, Edden RAE. The macromolecular MR spectrum does not change with healthy aging. Magn Reson Med 2021; 87:1711-1719. [PMID: 34841564 DOI: 10.1002/mrm.29093] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/11/2021] [Accepted: 11/02/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE To acquire the mobile macromolecule (MM) spectrum from healthy participants, and to investigate changes in the signals with age and sex. METHODS 102 volunteers (49 M/53 F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Spectral data were acquired at 3T using PRESS localization with a voxel size of 30 × 26 × 26 mm3 , pre-inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite-nulled spectra were modeled to eliminate residual metabolite signals, which were then subtracted out to yield a "clean" MM spectrum using the Osprey software. Pearson's correlation coefficient was calculated between integrals and age for the 14 MM signals. One-way ANOVA was performed to determine differences between age groups. An independent t-test was carried out to determine differences between sexes. RESULTS MM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM signals. One-way ANOVA also suggested no age-group differences for any MM peak (all p > .004). No differences were observed between sex groups. WM and GM voxel fractions showed a significant (p < .05) negative linear association with age in the WM-predominant CSO (R = -0.29) and GM-predominant PCC regions (R = -0.57) respectively while CSF increased significantly with age in both regions. CONCLUSION Our findings suggest that a pre-defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups.
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Affiliation(s)
- Steve C N Hui
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Tao Gong
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Helge J Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Mark Mikkelsen
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Sofie Tapper
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yufan Chen
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Muhammad G Saleh
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Eric C Porges
- Center for Cognitive Aging and Memory, University of Florida, Gainesville, Florida, USA.,McKnight Brain Research Foundation, University of Florida, Gainesville, Florida, USA.,Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA
| | | | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Richard A E Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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12
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Wright AM, Murali-Manohar S, Borbath T, Avdievich NI, Henning A. Relaxation-corrected macromolecular model enables determination of 1 H longitudinal T 1 -relaxation times and concentrations of human brain metabolites at 9.4T. Magn Reson Med 2021; 87:33-49. [PMID: 34374449 DOI: 10.1002/mrm.28958] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE Ultrahigh field MRS has improved characterization of the neurochemical profile. To compare results obtained at 9.4T to those from lower field strengths, it is of interest to quantify the concentrations of metabolites measured. Thus, measuring T1 -relaxation times is necessary to correct for T1 -weighting that occurs in acquisitions for single-voxel spectroscopy and spectroscopic imaging. A macromolecule (MM) simulation model was developed to fit MM contributions to the short TE inversion series used to measure T1 -relaxation times. METHODS An inversion series with seven time points was acquired with metabolite-cycled STEAM to estimate T1 -relaxation times of metabolites. A short TE was employed in this study to retain signals from metabolites with short T2 -relaxation times and J-couplings. The underlying macromolecule spectrum was corrected by developing a sequence-specific, relaxation-corrected simulated MM model. Quantification of metabolite peaks was performed using internal water referencing and relaxation corrections. RESULTS T1 -relaxation times for metabolites range from approximately 750 to approximately 2000 ms and approximately 1000 to approximately 2400 ms in gray matter (GM)- and white matter (WM)- rich voxels, respectively. Quantification of metabolites was compared between GM and WM voxels, as well as between results that used a simulated MM spectrum against those that used an experimentally acquired MM spectrum. Metabolite concentrations are reported in mmol/kg quantities. CONCLUSION T1 -relaxation times are reported for nonsinglet resonances for the first time at 9.4T by use of a MM simulation model to account for contributions from the MM spectrum. In addition to T1 -relaxation times, quantification results of metabolites from GM- and WM-rich voxels are reported.
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Affiliation(s)
- Andrew Martin Wright
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive & Systems Neuroscience, Tübingen, Germany
| | - Saipavitra Murali-Manohar
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Tamas Borbath
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Nikolai I Avdievich
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - 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, USA
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13
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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 IN BIOMEDICINE 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] [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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14
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Zöllner HJ, Považan M, Hui SC, Tapper S, Edden RA, Oeltzschner G. Comparison of different linear-combination modeling algorithms for short-TE proton spectra. NMR IN BIOMEDICINE 2021; 34:e4482. [PMID: 33530131 PMCID: PMC8935349 DOI: 10.1002/nbm.4482] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 01/09/2021] [Indexed: 05/08/2023]
Abstract
Short-TE proton MRS is used to study metabolism in the human brain. Common analysis methods model the data as a linear combination of metabolite basis spectra. This large-scale multi-site study compares the levels of the four major metabolite complexes in short-TE spectra estimated by three linear-combination modeling (LCM) algorithms. 277 medial parietal lobe short-TE PRESS spectra (TE = 35 ms) from a recent 3 T multi-site study were preprocessed with the Osprey software. The resulting spectra were modeled with Osprey, Tarquin and LCModel, using the same three vendor-specific basis sets (GE, Philips and Siemens) for each algorithm. Levels of total N-acetylaspartate (tNAA), total choline (tCho), myo-inositol (mI) and glutamate + glutamine (Glx) were quantified with respect to total creatine (tCr). Group means and coefficient of variations of metabolite estimates agreed well for tNAA and tCho across vendors and algorithms, but substantially less so for Glx and mI, with mI systematically estimated as lower by Tarquin. The cohort mean coefficient of determination for all pairs of LCM algorithms across all datasets and metabolites was R 2 ¯ = 0.39, indicating generally only moderate agreement of individual metabolite estimates between algorithms. There was a significant correlation between local baseline amplitude and metabolite estimates (cohort mean R 2 ¯ = 0.10). While mean estimates of major metabolite complexes broadly agree between linear-combination modeling algorithms at group level, correlations between algorithms are only weak-to-moderate, despite standardized preprocessing, a large sample of young, healthy and cooperative subjects, and high spectral quality. These findings raise concerns about the comparability of MRS studies, which typically use one LCM software and much smaller sample sizes.
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Affiliation(s)
- Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Michal Považan
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Steve C.N. Hui
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Sofie Tapper
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Richard A.E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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15
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Murali-Manohar S, Wright AM, Borbath T, Avdievich NI, Henning A. A novel method to measure T 1 -relaxation times of macromolecules and quantification of the macromolecular resonances. Magn Reson Med 2020; 85:601-614. [PMID: 32864826 DOI: 10.1002/mrm.28484] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 07/29/2020] [Accepted: 07/29/2020] [Indexed: 01/29/2023]
Abstract
PURPOSE Macromolecular peaks underlying metabolite spectra influence the quantification of metabolites. Therefore, it is important to understand the extent of contribution from macromolecules (MMs) in metabolite quantification. However, to model MMs more accurately in spectral fitting, differences in T1 relaxation times among individual MM peaks must be considered. Characterization of T1 -relaxation times for all individual MM peaks using a single inversion recovery technique is difficult due to eventual contributions from metabolites. On the contrary, a double inversion recovery (DIR) technique provided flexibility to acquire MM spectra spanning a range of longitudinal magnetizations with minimal metabolite influence. Thus, a novel method to determine T1 -relaxation times of individual MM peaks is reported in this work. METHODS Extensive Bloch simulations were performed to determine inversion time combinations for a DIR technique that yielded adequate MM signal with varying longitudinal magnetizations while minimizing metabolite contributions. MM spectra were acquired using DIR-metabolite-cycled semi-LASER sequence. LCModel concentrations were fitted to the DIR signal equation to calculate T1 -relaxation times. RESULTS T1 -relaxation times of MMs range from 204 to 510 ms and 253 to 564 ms in gray- and white-matter rich voxels respectively at 9.4T. Additionally, concentrations of 13 MM peaks are reported. CONCLUSION A novel DIR method is reported in this work to calculate T1 -relaxation times of MMs in the human brain. T1 -relaxation times and relaxation time corrected concentrations of individual MMs are reported in gray- and white-matter rich voxels for the first time at 9.4T.
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Affiliation(s)
- Saipavitra Murali-Manohar
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Andrew Martin Wright
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive & Systems Neuroscience, Tübingen, Germany
| | - Tamas Borbath
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Nikolai I Avdievich
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Anke Henning
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA
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16
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Borbath T, Murali-Manohar S, Wright AM, Henning A. In vivo characterization of downfield peaks at 9.4 T: T 2 relaxation times, quantification, pH estimation, and assignments. Magn Reson Med 2020; 85:587-600. [PMID: 32783249 DOI: 10.1002/mrm.28442] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 07/01/2020] [Accepted: 07/01/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE Relaxation times are a valuable asset when determining spectral assignments. In this study, apparent T2 relaxation times ( T 2 app ) of downfield peaks are reported in the human brain at 9.4 T and are used to guide spectral assignments of some downfield metabolite peaks. METHODS Echo time series of downfield metabolite spectra were acquired at 9.4 T using a metabolite-cycled semi-LASER sequence. Metabolite spectral fitting was performed using LCModel V6.3-1L while fitting a pH sweep to estimate the pH of the homocarnosine (hCs) imidazole ring. T 2 app were calculated by fitting the resulting relative amplitudes of the peaks to a mono-exponential decay across the TE series. Furthermore, estimated tissue concentrations of molecules were calculated using the relaxation times and internal water as a reference. RESULTS T 2 app of downfield metabolites are reported within a range from 16 to 32 ms except for homocarnosine with T 2 app of 50 ms. Correcting T 2 app for exchange rates ( T 2 c o r r ) resulted in relaxation times between 20 and 33 ms. The estimated pH values based on hCs imidazole range from 7.07 to 7.12 between subjects. Furthermore, analyzing the linewidths of the downfield peaks and their T 2 app contribution led to possible peak assignments. CONCLUSION T 2 app relaxation times were longer for the assigned metabolite peaks compared to the unassigned peaks. Tissue pH estimation in vivo with proton MRS and simultaneous quantification of amide protons at 8.30 ± 0.15 ppm is likely possible. Based on concentration, linewidth, and exchange rates measurements, tentative peak assignments are discussed for adenosine triphosphate (ATP), N-acetylaspartylglutamate (NAAG), and urea.
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Affiliation(s)
- Tamas Borbath
- High-Field Magnetic Resonance, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Saipavitra Murali-Manohar
- High-Field Magnetic Resonance, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Andrew Martin Wright
- High-Field Magnetic Resonance, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive & Systems Neuroscience, Tübingen, Germany
| | - Anke Henning
- High-Field Magnetic Resonance, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.,Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA
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17
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Murali‐Manohar S, Borbath T, Wright AM, Soher B, Mekle R, Henning A. T
2
relaxation times of macromolecules and metabolites in the human brain at 9.4 T. Magn Reson Med 2020; 84:542-558. [DOI: 10.1002/mrm.28174] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 12/06/2019] [Accepted: 12/27/2019] [Indexed: 11/10/2022]
Affiliation(s)
- Saipavitra Murali‐Manohar
- High‐Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tübingen Germany
- Faculty of Science University of Tübingen Tübingen Germany
| | - Tamas Borbath
- High‐Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tübingen Germany
- Faculty of Science University of Tübingen Tübingen Germany
| | - Andrew Martin Wright
- High‐Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tübingen Germany
- IMPRS for Cognitive & Systems Neuroscience Tübingen Germany
| | - Brian Soher
- Radiology Duke University Medical Center Durham North Carolina
| | - Ralf Mekle
- Center for Stroke Research Berlin (CSB) Charité ‐ Universitätsmedizin Berlin Berlin Germany
| | - Anke Henning
- High‐Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tübingen Germany
- Advanced Imaging Research Center UT Southwestern Medical Center Dallas Texas
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18
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Heckova E, Považan M, Strasser B, Motyka S, Hangel G, Hingerl L, Moser P, Lipka A, Gruber S, Trattnig S, Bogner W. Effects of different macromolecular models on reproducibility of FID-MRSI at 7T. Magn Reson Med 2020; 83:12-21. [PMID: 31393037 PMCID: PMC6851974 DOI: 10.1002/mrm.27922] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 06/12/2019] [Accepted: 07/08/2019] [Indexed: 12/25/2022]
Abstract
PURPOSE A properly characterized macromolecular (MM) contribution is essential for accurate metabolite quantification in FID-MRSI. MM information can be included into the fitting model as a single component or parameterized and included over several individual MM resonances, which adds flexibility when pathologic changes are present but is prone to potential overfitting. This study investigates the effects of different MM models on MRSI reproducibility. METHODS Clinically feasible, high-resolution FID-MRSI data were collected in ~5 min at 7 Tesla from 10 healthy volunteers and quantified via LCModel (version 6.3) with 3 basis sets, each with a different approach for how the MM signal was handled: averaged measured whole spectrum (full MM), 9 parameterized components (param MM) with soft constraints to avoid overparameterization, or without any MM information included in the fitting prior knowledge. The test-retest reproducibility of MRSI scans was assessed voxel-wise using metabolite coefficients of variation and intraclass correlation coefficients and compared between the basis sets. Correlations of concentration estimates were investigated for the param MM fitting model. RESULTS The full MM model provided the most reproducible quantification of total NAA, total Cho, myo-inositol, and glutamate + glutamine ratios to total Cr (coefficients of variations ≤ 8%, intraclass correlation coefficients ≥ 0.76). Using the param MM model resulted in slightly lower reproducibility (up to +3% higher coefficients of variations, up to -0.1 decreased intraclass correlation coefficients). The quantification of the parameterized macromolecules did not affect quantification of the overlapping metabolites. CONCLUSION Clinically feasible FID-MRSI with an experimentally acquired MM spectrum included in prior knowledge provides highly reproducible quantification for the most common neurometabolites in healthy volunteers. Parameterization of the MM spectrum may be preferred as a compromise between quantification accuracy and reproducibility when the MM content is expected to be pathologically altered.
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Affiliation(s)
- Eva Heckova
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Michal Považan
- Russell H. Morgan Department of Radiology and Radiological Science, The John Hopkins University School of Medicine, Baltimore, Maryland
| | - Bernhard Strasser
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Stanislav Motyka
- 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
| | - Lukas Hingerl
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Philipp Moser
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Alexandra Lipka
- High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, 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.,Christian Doppler Laboratory for Clinical Molecular MR Imaging, 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 Clinical Molecular MR Imaging, Vienna, Austria
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Giapitzakis I, Borbath T, Murali‐Manohar S, Avdievich N, Henning A. Investigation of the influence of macromolecules and spline baseline in the fitting model of human brain spectra at 9.4T. Magn Reson Med 2018; 81:746-758. [DOI: 10.1002/mrm.27467] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 06/10/2018] [Accepted: 07/05/2018] [Indexed: 11/10/2022]
Affiliation(s)
- Ioannis‐Angelos Giapitzakis
- High‐Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics Tübingen Germany
- IMPRS for Cognitive and Systems Neuroscience Tübingen Germany
| | - Tamas Borbath
- High‐Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics Tübingen Germany
- Faculty of Science University of Tübingen Tübingen Germany
| | - Saipavitra Murali‐Manohar
- High‐Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics Tübingen Germany
- Faculty of Science University of Tübingen Tübingen Germany
| | - Nikolai Avdievich
- High‐Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics Tübingen Germany
- Institute of Physics University of Greifswald Greifswald Germany
| | - Anke Henning
- High‐Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics Tübingen Germany
- Institute of Physics University of Greifswald Greifswald Germany
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Mikkelsen M, Harris AD, Edden RAE, Puts NAJ. Macromolecule-suppressed GABA measurements correlate more strongly with behavior than macromolecule-contaminated GABA+ measurements. Brain Res 2018; 1701:204-211. [PMID: 30244020 DOI: 10.1016/j.brainres.2018.09.021] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 09/12/2018] [Accepted: 09/18/2018] [Indexed: 10/28/2022]
Abstract
The inhibitory neurotransmitter γ-aminobutyric acid (GABA) is known to be fundamental to the neuronal processes underlying visual orientation and vibrotactile frequency and amplitude discrimination. Previous studies have demonstrated that performance on visual and vibrotactile psychophysics tasks is associated with in vivo measurements of "GABA+" levels - a measure of GABA substantially contaminated by a macromolecular (MM) signal. Here, we establish that these prior findings are indeed driven by the GABA fraction of that signal. Edited magnetic resonance spectroscopy (MRS) was used to measure GABA with and without MM suppression in the sensorimotor (SM1) and occipital cortices in 14 healthy male adults. Volunteers also underwent psychophysical experiments to assess their performance on visual orientation discrimination and vibrotactile amplitude and frequency discrimination. We show that MM-suppressed GABA levels correlate more strongly with individual differences in vibrotactile (in the case of SM1 GABA; amplitude: r = -0.63, p = 0.03; frequency: r = -0.62, p = 0.02) and visual orientation (in the case of occipital GABA; r = -0.59, p = 0.05) discrimination thresholds than GABA levels contaminated by MM (vibrotactile amplitude: r = -0.36, p = 0.30; vibrotactile frequency: r = -0.53, p = 0.09; visual orientation: r = 0.21, p = 0.55). These findings further support the view that measurements of endogenous GABA acquired with edited MRS can usefully probe neurochemical-behavioral relationships in humans. Moreover, the more specific measurement of GABA used in this study provides increased statistical power to observe these regionally specific relationships.
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Affiliation(s)
- 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
| | - Ashley D Harris
- Department of Radiology, University of Calgary, Calgary, AB, Canada; Child and Adolescent Imaging Research (CAIR) Program, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - 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
| | - Nicolaas A J Puts
- 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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Steel A, Chiew M, Jezzard P, Voets NL, Plaha P, Thomas MA, Stagg CJ, Emir UE. Metabolite-cycled density-weighted concentric rings k-space trajectory (DW-CRT) enables high-resolution 1 H magnetic resonance spectroscopic imaging at 3-Tesla. Sci Rep 2018; 8:7792. [PMID: 29773892 PMCID: PMC5958083 DOI: 10.1038/s41598-018-26096-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Accepted: 05/02/2018] [Indexed: 11/10/2022] Open
Abstract
Magnetic resonance spectroscopic imaging (MRSI) is a promising technique in both experimental and clinical settings. However, to date, MRSI has been hampered by prohibitively long acquisition times and artifacts caused by subject motion and hardware-related frequency drift. In the present study, we demonstrate that density weighted concentric ring trajectory (DW-CRT) k-space sampling in combination with semi-LASER excitation and metabolite-cycling enables high-resolution MRSI data to be rapidly acquired at 3 Tesla. Single-slice full-intensity MRSI data (short echo time (TE) semi-LASER TE = 32 ms) were acquired from 6 healthy volunteers with an in-plane resolution of 5 × 5 mm in 13 min 30 sec using this approach. Using LCModel analysis, we found that the acquired spectra allowed for the mapping of total N-acetylaspartate (median Cramer-Rao Lower Bound [CRLB] = 3%), glutamate+glutamine (8%), and glutathione (13%). In addition, we demonstrate potential clinical utility of this technique by optimizing the TE to detect 2-hydroxyglutarate (long TE semi-LASER, TE = 110 ms), to produce relevant high-resolution metabolite maps of grade III IDH-mutant oligodendroglioma in a single patient. This study demonstrates the potential utility of MRSI in the clinical setting at 3 Tesla.
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Affiliation(s)
- Adam Steel
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Mark Chiew
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Peter Jezzard
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Natalie L Voets
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
- Department of Neurosurgery, John Radcliffe Hospital, Oxford, United Kingdom
| | - Puneet Plaha
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
- Department of Neurosurgery, John Radcliffe Hospital, Oxford, United Kingdom
| | - Michael Albert Thomas
- Department of Radiological Sciences, University of California, Los Angeles, California, USA
| | - Charlotte J Stagg
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
| | - Uzay E Emir
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK.
- Purdue University, School of Health Sciences, West Lafayette, IN, 47907, USA.
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