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Turco F, Capiglioni M, Weng G, Slotboom J. TensorFit: A torch-based tool for ultrafast metabolite fitting of large MRSI data sets. Magn Reson Med 2024; 92:447-458. [PMID: 38469890 DOI: 10.1002/mrm.30084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/21/2024] [Accepted: 02/26/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE To introduce a tool (TensorFit) for ultrafast and robust metabolite fitting of MRSI data based on Torch's auto-differentiation and optimization framework. METHODS TensorFit was implemented in Python based on Torch's auto-differentiation to fit individual metabolites in MRS spectra. The underlying time domain and/or frequency domain fitting model is based on a linear combination of metabolite spectroscopic response. The computational time efficiency and accuracy of TensorFit were tested on simulated and in vivo MRS data and compared against TDFDFit and QUEST. RESULTS TensorFit demonstrates a significant improvement in computation speed, achieving a 165-times acceleration compared with TDFDFit and 115 times against QUEST. TensorFit showed smaller percentual errors on simulated data compared with TDFDFit and QUEST. When tested on in vivo data, it performed similarly to TDFDFit with a 2% better fit in terms of mean squared error while obtaining a 169-fold speedup. CONCLUSION TensorFit enables fast and robust metabolite fitting in large MRSI data sets compared with conventional metabolite fitting methods. This tool could boost the clinical applicability of large 3D MRSI by enabling the fitting of large MRSI data sets within computation times acceptable in a clinical environment.
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Affiliation(s)
- Federico Turco
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Milena Capiglioni
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Guodong Weng
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
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Morelli M, Dudzikowska K, Deelchand DK, Quinn AJ, Mullins PG, Apps MAJ, Wilson M. Functional Magnetic Resonance Spectroscopy of Prolonged Motor Activation using Conventional and Spectral GLM Analyses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.15.594270. [PMID: 38798416 PMCID: PMC11118477 DOI: 10.1101/2024.05.15.594270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Background Functional MRS (fMRS) is a technique used to measure metabolic changes in response to increased neuronal activity, providing unique insights into neurotransmitter dynamics and neuroenergetics. In this study we investigate the response of lactate and glutamate levels in the motor cortex during a sustained motor task using conventional spectral fitting and explore the use of a novel analysis approach based on the application of linear modelling directly to the spectro-temporal fMRS data. Methods fMRS data were acquired at a field strength of 3 Tesla from 23 healthy participants using a short echo-time (28ms) semi-LASER sequence. The functional task involved rhythmic hand clenching over a duration of 8 minutes and standard MRS preprocessing steps, including frequency and phase alignment, were employed. Both conventional spectral fitting and direct linear modelling were applied, and results from participant-averaged spectra and metabolite-averaged individual analyses were compared. Results We observed a 20% increase in lactate in response to the motor task, consistent with findings at higher magnetic field strengths. However, statistical testing showed some variability between the two averaging schemes and fitting algorithms. While lactate changes were supported by the direct spectral modelling approach, smaller increases in glutamate (2%) were inconsistent. Exploratory spectral modelling identified a 4% decrease in aspartate, aligning with conventional fitting and observations from prolonged visual stimulation. Conclusion We demonstrate that lactate dynamics in response to a prolonged motor task are observed using short-echo time semi-LASER at 3 Tesla, and that direct linear modelling of fMRS data is a useful complement to conventional analysis. Future work includes mitigating spectral confounds, such as scalp lipid contamination and lineshape drift, and further validation of our novel direct linear modelling approach through experimental and simulated datasets.
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Affiliation(s)
- Maria Morelli
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Katarzyna Dudzikowska
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Dinesh K. Deelchand
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Andrew J. Quinn
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | | | - Matthew A. J. Apps
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Martin Wilson
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
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Simicic D, Zöllner HJ, Davies-Jenkins CW, Hupfeld KE, Edden RAE, Oeltzschner G. Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.26.586804. [PMID: 38585798 PMCID: PMC10996641 DOI: 10.1101/2024.03.26.586804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Purpose Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a two-dimensional linear-combination model (2D-LCM) of individual transients ('model-based FPC'). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates. Methods We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the standard deviation of those ground-truth errors, and amplitude Cramér Rao Lower Bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data. Results 2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of frequency and phase correction and amplitudes performed substantially better at low-to-very-low SNR. Conclusion Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, e.g., long TEs or strong diffusion weighting.
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Affiliation(s)
- Dunja Simicic
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Christopher W. Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Kathleen E. Hupfeld
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Richard A. E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
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Ligneul C, Najac C, Döring A, Beaulieu C, Branzoli F, Clarke WT, Cudalbu C, Genovese G, Jbabdi S, Jelescu I, Karampinos D, Kreis R, Lundell H, Marjańska M, Möller HE, Mosso J, Mougel E, Posse S, Ruschke S, Simsek K, Szczepankiewicz F, Tal A, Tax C, Oeltzschner G, Palombo M, Ronen I, Valette J. Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling. Magn Reson Med 2024; 91:860-885. [PMID: 37946584 DOI: 10.1002/mrm.29877] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/18/2023] [Accepted: 09/08/2023] [Indexed: 11/12/2023]
Abstract
Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.
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Affiliation(s)
- Clémence Ligneul
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Chloé Najac
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - André Döring
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Christian Beaulieu
- Departments of Biomedical Engineering and Radiology, University of Alberta, Alberta, Edmonton, Canada
| | - Francesca Branzoli
- Paris Brain Institute-ICM, Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Cristina Cudalbu
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Guglielmo Genovese
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minnesota, Minneapolis, USA
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Ileana Jelescu
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dimitrios Karampinos
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Roland Kreis
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager anf Hvidovre, Hvidovre, Denmark
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minnesota, Minneapolis, USA
| | - Harald E Möller
- NMR Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jessie Mosso
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- LIFMET, EPFL, Lausanne, Switzerland
| | - Eloïse Mougel
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoires des Maladies Neurodégénératives, Fontenay-aux-Roses, France
| | - Stefan Posse
- Department of Neurology, University of New Mexico School of Medicine, New Mexico, Albuquerque, USA
- Department of Physics and Astronomy, University of New Mexico School of Medicine, New Mexico, Albuquerque, USA
| | - Stefan Ruschke
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Kadir Simsek
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | | | - Assaf Tal
- Department of Chemical and Biological Physics, The Weizmann Institute of Science, Rehovot, Israel
| | - Chantal Tax
- University Medical Center Utrecht, Utrecht, The Netherlands
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Maryland, Baltimore, USA
- F. M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Maryland, Baltimore, USA
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Itamar Ronen
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, UK
| | - Julien Valette
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoires des Maladies Neurodégénératives, Fontenay-aux-Roses, France
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Zöllner HJ, Davies-Jenkins C, Simicic D, Tal A, Sulam J, Oeltzschner G. Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565164. [PMID: 38260650 PMCID: PMC10802456 DOI: 10.1101/2023.11.01.565164] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Purpose The interest in applying and modeling dynamic MRS has recently grown. 2D modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to 1D modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches. Methods Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multi-transient LCM with 1D-LCM of the average. 2,500 datasets per condition with different noise representations of a 64-transient MRS experiment at 6 signal-to-noise levels for two separate spin systems (scyllo-inositol and GABA) were analyzed. Additional datasets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by standard deviations and Cramér-Rao Lower Bounds (CRLB). Results Amplitude estimates for 1D- and 2D-LCM agreed well and showed similar level of bias compared to the ground truth. Estimated CRLBs agreed well between both models and with ground truth CRLBs. For correlated noise the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM. Conclusion Our results indicate that the model performance of 2D multi-transient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as necessary basis for further applications of 2D modeling.
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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, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Christopher Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Dunja Simicic
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Jeremias Sulam
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, United States
- Mathematical Institute for Data Science, The Johns Hopkins University, Baltimore, MD, United States
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
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Steiner L, Muri R, Wijesinghe D, Jann K, Maissen-Abgottspon S, Radojewski P, Pospieszny K, Kreis R, Kiefer C, Hochuli M, Trepp R, Everts R. Cerebral blood flow and white matter alterations in adults with phenylketonuria. Neuroimage Clin 2023; 41:103550. [PMID: 38091797 PMCID: PMC10716784 DOI: 10.1016/j.nicl.2023.103550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/15/2023] [Accepted: 12/08/2023] [Indexed: 03/16/2024]
Abstract
BACKGROUND Phenylketonuria (PKU) represents a congenital metabolic defect that disrupts the process of converting phenylalanine (Phe) into tyrosine. Earlier investigations have revealed diminished cognitive performance and changes in brain structure and function (including the presence of white matter lesions) among individuals affected by PKU. However, there exists limited understanding regarding cerebral blood flow (CBF) and its potential associations with cognition, white matter lesions, and metabolic parameters in patients with PKU, which we therefore aimed to investigate in this study. METHOD Arterial spin labeling perfusion MRI was performed to measure CBF in 30 adults with early-treated classical PKU (median age 35.5 years) and 59 healthy controls (median age 30.0 years). For all participants, brain Phe levels were measured with 1H spectroscopy, and white matter lesions were rated by two neuroradiologists on T2 weighted images. White matter integrity was examined with diffusion tensor imaging (DTI). For patients only, concurrent plasma Phe levels were assessed after an overnight fasting period. Furthermore, past Phe levels were collected to estimate historical metabolic control. On the day of the MRI, each participant underwent a cognitive assessment measuring IQ and performance in executive functions, attention, and processing speed. RESULTS No significant group difference was observed in global CBF between patients and controls (F (1, 87) = 3.81, p = 0.054). Investigating CBF on the level of cerebral arterial territories, reduced CBF was observed in the left middle and posterior cerebral artery (MCA and PCA), with the most prominent reduction of CBF in the anterior subdivision of the MCA (F (1, 87) = 6.15, p = 0.015, surviving FDR correction). White matter lesions in patients were associated with cerebral blood flow reduction in the affected structure. Particularly, patients with lesions in the occipital lobe showed significant CBF reductions in the left PCA (U = 352, p = 0.013, surviving FDR correction). Additionally, axial diffusivity measured with DTI was positively associated with CBF in the ACA and PCA (surviving FDR correction). Cerebral blood flow did not correlate with cognitive performance or metabolic parameters. CONCLUSION The relationship between cerebral blood flow and white matter indicates a complex interplay between vascular health and white matter alterations in patients with PKU. It highlights the importance of considering a multifactorial model when investigating the impact of PKU on the brain.
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Affiliation(s)
- Leonie Steiner
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland; Division of Neuropaediatrics, Development and Rehabilitation, Department of Paediatrics, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Raphaela Muri
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland; Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Dilmini Wijesinghe
- Laboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, USA
| | - Kay Jann
- Laboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, USA
| | - Stephanie Maissen-Abgottspon
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Katarzyna Pospieszny
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Roland Kreis
- Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Claus Kiefer
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Michel Hochuli
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Roman Trepp
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Regula Everts
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland; Division of Neuropaediatrics, Development and Rehabilitation, Department of Paediatrics, Inselspital, Bern University Hospital and University of Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.
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Davies-Jenkins CW, Döring A, Fasano F, Kleban E, Mueller L, Evans CJ, Afzali M, Jones DK, Ronen I, Branzoli F, Tax CMW. Practical considerations of diffusion-weighted MRS with ultra-strong diffusion gradients. Front Neurosci 2023; 17:1258408. [PMID: 38144210 PMCID: PMC10740196 DOI: 10.3389/fnins.2023.1258408] [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: 07/14/2023] [Accepted: 11/03/2023] [Indexed: 12/26/2023] Open
Abstract
Introduction Diffusion-weighted magnetic resonance spectroscopy (DW-MRS) offers improved cellular specificity to microstructure-compared to water-based methods alone-but spatial resolution and SNR is severely reduced and slow-diffusing metabolites necessitate higher b-values to accurately characterize their diffusion properties. Ultra-strong gradients allow access to higher b-values per-unit time, higher SNR for a given b-value, and shorter diffusion times, but introduce additional challenges such as eddy-current artefacts, gradient non-uniformity, and mechanical vibrations. Methods In this work, we present initial DW-MRS data acquired on a 3T Siemens Connectom scanner equipped with ultra-strong (300 mT/m) gradients. We explore the practical issues associated with this manner of acquisition, the steps that may be taken to mitigate their impact on the data, and the potential benefits of ultra-strong gradients for DW-MRS. An in-house DW-PRESS sequence and data processing pipeline were developed to mitigate the impact of these confounds. The interaction of TE, b-value, and maximum gradient amplitude was investigated using simulations and pilot data, whereby maximum gradient amplitude was restricted. Furthermore, two DW-MRS voxels in grey and white matter were acquired using ultra-strong gradients and high b-values. Results Simulations suggest T2-based SNR gains that are experimentally confirmed. Ultra-strong gradient acquisitions exhibit similar artefact profiles to those of lower gradient amplitude, suggesting adequate performance of artefact mitigation strategies. Gradient field non-uniformity influenced ADC estimates by up to 4% when left uncorrected. ADC and Kurtosis estimates for tNAA, tCho, and tCr align with previously published literature. Discussion In conclusion, we successfully implemented acquisition and data processing strategies for ultra-strong gradient DW-MRS and results indicate that confounding effects of the strong gradient system can be ameliorated, while achieving shorter diffusion times and improved metabolite SNR.
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Affiliation(s)
- Christopher W. Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - André Döring
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- CIBM Center for Biomedical Imaging, EPFL CIBM-AIT, EPFL Lausanne, Lausanne, Switzerland
| | - Fabrizio Fasano
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Siemens Healthcare Ltd., Camberly, United Kingdom
| | - Elena Kleban
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Department of Radiology, Universität Bern, Bern, Switzerland
| | - Lars Mueller
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Leeds Institute of Cardiovascular & Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - C. John Evans
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Leeds Institute of Cardiovascular & Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Itamar Ronen
- Clinical Sciences Institue, Brighton and Sussex Medical School, Brighton, United Kingdom
| | - Francesca Branzoli
- Center for NeuroImaging Research (CENIR), Paris Brain Institute (ICM), Pitié-Salpêtrière Hospital, Paris, France
- Inserm U1127, CNRS U7225, Sorbonne Universités, Paris, France
| | - Chantal M. W. Tax
- Brain Research Imaging Centre, School Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
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Dziadosz M, Rizzo R, Kyathanahally SP, Kreis R. Denoising single MR spectra by deep learning: Miracle or mirage? Magn Reson Med 2023; 90:1749-1761. [PMID: 37332185 DOI: 10.1002/mrm.29762] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 05/05/2023] [Accepted: 05/28/2023] [Indexed: 06/20/2023]
Abstract
PURPOSE The inherently poor SNR of MRS measurements presents a significant hurdle to its clinical application. Denoising by machine or deep learning (DL) was proposed as a remedy. It is investigated whether such denoising leads to lower estimate uncertainties or whether it essentially reduces noise in signal-free areas only. METHODS Noise removal based on supervised DL with U-nets was implemented using simulated 1 H MR spectra of human brain in two approaches: (1) via time-frequency domain spectrograms and (2) using 1D spectra as input. Quality of denoising was evaluated in three ways: (1) by an adapted fit quality score, (2) by traditional model fitting, and (3) by quantification via neural networks. RESULTS Visually appealing spectra were obtained; hinting that denoising is well-suited for MRS. However, an adapted denoising score showed that noise removal is inhomogeneous and more efficient for signal-free areas. This was confirmed by quantitative analysis of traditional fit results as well as DL quantitation following DL denoising. DL denoising, although apparently successful as judged by mean squared errors, led to substantially biased estimates in both implementations. CONCLUSION The implemented DL-based denoising techniques may be useful for display purposes, but do not help quantitative evaluations, confirming expectations based on estimation theory: Cramér Rao lower bounds defined by the original data and the appropriate fitting model cannot be circumvented in an unbiased way for single data sets, unless additional prior knowledge can be incurred in the form of parameter restrictions/relations or applicable substates.
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Affiliation(s)
- Martyna Dziadosz
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Rudy Rizzo
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Sreenath P Kyathanahally
- Department System Analysis, Integrated Assessment and Modelling, Eawag - Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Roland Kreis
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
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9
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Huang YL, Lin YR, Tsai SY. Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy. MAGMA (NEW YORK, N.Y.) 2023:10.1007/s10334-023-01120-z. [PMID: 37713007 DOI: 10.1007/s10334-023-01120-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/09/2023] [Accepted: 08/30/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have found a variety of applications on the process of MRS data. A quantification strategy compatible to DL base MRS spectral processing method is, therefore, useful. MATERIALS AND METHODS This study aims to investigate whether metabolite concentrations quantified using a convolutional neural network (CNN) based method, coupled with a scaling procedure that normalizes spectral signals for CNN input and linear regression, can effectively reflect variations in metabolite concentrations in IU across different brain regions with varying signal-to-noise ratios (SNR) and linewidths (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels associated with metabolite predictions. In vivo MRS spectra were acquired from three brain regions of 43 subjects using a 3T system. RESULTS The metabolite concentrations in IU of five major metabolites, quantified using CNN and LCModel, exhibit similar ranges with Pearson's correlation coefficients ranging from 0.24 to 0.78. The SE of the metabolites shows a positive correlation with Cramer-Rao lower bound (CRLB) (r=0.46) and absolute CRLB (r=0.81), calculated by multiplying CRLBs with the quantified metabolite content. CONCLUSION In conclusion, the CNN based method with the proposed scaling procedures can be employed to quantify in vivo MRS spectra and derive metabolites concentrations in IU. The SE can be used as error index, indicating predicted uncertainties for metabolites and sharing information similar to the absolute CRLB.
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Affiliation(s)
- Yu-Long Huang
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Yi-Ru Lin
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Shang-Yueh Tsai
- Graduate Institute of Applied Physics, National Chengchi University, No.64, Sec.2, ZhiNan Rd., Wenshan District, Taipei, 11605, Taiwan.
- Research Center for Mind, Brain and Learning, National Chengchi University, Taipei, Taiwan.
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10
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Soher BJ, Semanchuk P, Todd D, Ji X, Deelchand D, Joers J, Oz G, Young K. Vespa: Integrated applications for RF pulse design, spectral simulation and MRS data analysis. Magn Reson Med 2023. [PMID: 37183778 DOI: 10.1002/mrm.29686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 03/24/2023] [Accepted: 04/12/2023] [Indexed: 05/16/2023]
Abstract
PURPOSE The Vespa package (Versatile Simulation, Pulses, and Analysis) is described and demonstrated. It provides workflows for developing and optimizing linear combination modeling (LCM) fitting for 1 H MRS data using intuitive graphical user interface interfaces for RF pulse design, spectral simulation, and MRS data analysis. Command line interfaces for embedding workflows in MR manufacturer platforms and utilities for synthetic dataset creation are included. Complete provenance is maintained for all steps in workflows. THEORY AND METHODS Vespa is written in Python for compatibility across operating systems. It embeds the PyGAMMA spectral simulation library for spectral simulation. Multiprocessing methods accelerate processing and visualization. Applications use the Vespa database for results storage and cross-application access. Three projects demonstrate pulse, sequence, simulation, and data analysis workflows: (1) short TE semi-LASER single-voxel spectroscopy (SVS) LCM fitting, (2) optimizing MEGA-PRESS (MEscher-GArwood Point RESolved Spectroscopy) flip angle and LCM fitting, and (3) creating a synthetic short TE dataset. RESULTS The LCM workflows for in vivo basis set creation and spectral analysis showed reasonable results for both the short TE semi-LASER and MEGA-PRESS. Examples of pulses, simulations, and data fitting are shown in Vespa application interfaces for various steps to demonstrate the interactive workflow. CONCLUSION Vespa provides an efficient and extensible platform for characterizing RF pulses, pulse design, spectral simulation optimization, and automated LCM fitting via an interactive platform. Modular design and command line interface make it easy to embed in other platforms. As open source, it is free to the MRS community for use and extension. Vespa source code and documentation are available through GitHub.
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Affiliation(s)
- Brian J Soher
- Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Philip Semanchuk
- Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - David Todd
- Center for Imaging of Neurodegenerative Disorders, University of California, San Francisco, CA, USA
| | - Xiao Ji
- Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Dinesh Deelchand
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - James Joers
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Gulin Oz
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Karl Young
- Department of Radiology, University of California, San Francisco, CA, USA
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Muri R, Maissen-Abgottspon S, Reed MB, Kreis R, Hoefemann M, Radojewski P, Pospieszny K, Hochuli M, Wiest R, Lanzenberger R, Trepp R, Everts R. Compromised white matter is related to lower cognitive performance in adults with phenylketonuria. Brain Commun 2023; 5:fcad155. [PMID: 37265600 PMCID: PMC10231812 DOI: 10.1093/braincomms/fcad155] [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: 12/23/2022] [Revised: 04/19/2023] [Accepted: 05/12/2023] [Indexed: 06/03/2023] Open
Abstract
Despite increasing knowledge about the effects of phenylketonuria on brain structure and function, it is uncertain whether white matter microstructure is affected and if it is linked to patients' metabolic control or cognitive performance. Thus, we quantitatively assessed white matter characteristics in adults with phenylketonuria and assessed their relationship to concurrent brain and blood phenylalanine levels, historical metabolic control and cognitive performance. Diffusion tensor imaging and 1H spectroscopy were performed in 30 adults with early-treated classical phenylketonuria (median age 35.5 years) and 54 healthy controls (median age 29.3 years). Fractional anisotropy and mean, axial and radial diffusivity were investigated using tract-based spatial statistics, and white matter lesion load was evaluated. Brain phenylalanine levels were measured with 1H spectroscopy whereas concurrent plasma phenylalanine levels were assessed after an overnight fast. Retrospective phenylalanine levels were collected to estimate historical metabolic control, and a neuropsychological evaluation assessed the performance in executive functions, attention and processing speed. Widespread reductions in mean diffusivity, axial diffusivity and fractional anisotropy occurred in patients compared to controls. Mean diffusivity and axial diffusivity were decreased in several white matter tracts and were most restricted in the optic radiation (effect size rrb = 0.66 to 0.78, P < 0.001) and posterior corona radiata (rrb = 0.83 to 0.90, P < 0.001). Lower fractional anisotropy was found in the optic radiation and posterior corona radiata (rrb = 0.43 to 0.49, P < 0.001). White matter microstructure in patients was significantly associated with cognition. Specifically, inhibition was related to axial diffusivity in the external capsule (rs = -0.69, P < 0.001) and the superior (rs = -0.58, P < 0.001) and inferior longitudinal fasciculi (rs = -0.60, P < 0.001). Cognitive flexibility was associated with mean diffusivity of the posterior limb of the internal capsule (rs = -0.62, P < 0.001), and divided attention correlated with fractional anisotropy of the external capsule (rs = -0.61, P < 0.001). Neither concurrent nor historical metabolic control was significantly associated with white matter microstructure. White matter lesions were present in 29 out of 30 patients (96.7%), most often in the parietal and occipital lobes. However, total white matter lesion load scores were unrelated to patients' cognitive performance and metabolic control. In conclusion, our findings demonstrate that white matter alterations in early-treated phenylketonuria persist into adulthood, are most prominent in the posterior white matter and are likely to be driven by axonal damage. Furthermore, diffusion tensor imaging metrics in adults with phenylketonuria were related to performance in attention and executive functions.
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Affiliation(s)
- Raphaela Muri
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, 3010 Bern, Switzerland
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, 3010 Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, 3012 Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, 3010 Bern, Switzerland
| | - Stephanie Maissen-Abgottspon
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, 3010 Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, 3010 Bern, Switzerland
| | - Murray Bruce Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Roland Kreis
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, 3010 Bern, Switzerland
- Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, 3010 Bern, Switzerland
| | - Maike Hoefemann
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, 3010 Bern, Switzerland
- Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, 3010 Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, 3010 Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, 3010 Bern, Switzerland
| | - Katarzyna Pospieszny
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, 3010 Bern, Switzerland
| | - Michel Hochuli
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, 3010 Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, 3010 Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, 3010 Bern, Switzerland
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, 1090 Vienna, Austria
| | | | - Regula Everts
- Correspondence to: Regula Everts Department of Diabetes, Endocrinology Nutritional Medicine and Metabolism, Inselspital Bern University Hospital and University of Bern Freiburgstrasse, Bern 3010, Switzerland E-mail:
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12
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Pfyffer D, Zimmermann S, Şimşek K, Kreis R, Freund P, Seif M. Magnetic resonance spectroscopy investigation in the right human hippocampus following spinal cord injury. Front Neurol 2023; 14:1120227. [PMID: 37251221 PMCID: PMC10213741 DOI: 10.3389/fneur.2023.1120227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 04/20/2023] [Indexed: 05/31/2023] Open
Abstract
Objective Preclinical studies have shown that cognitive impairments following spinal cord injury (SCI), such as impaired spatial memory, are linked to inflammation, neurodegeneration, and reduced neurogenesis in the right hippocampus. This cross-sectional study aims to characterize metabolic and macrostructural changes in the right hippocampus and their association to cognitive function in traumatic SCI patients. Methods Within this cross-sectional study, cognitive function was assessed in 28 chronic traumatic SCI patients and 18 age-, sex-, and education-matched healthy controls by a visuospatial and verbal memory test. A magnetic resonance spectroscopy (MRS) and structural MRI protocol was performed in the right hippocampus of both groups to quantify metabolic concentrations and hippocampal volume, respectively. Group comparisons investigated changes between SCI patients and healthy controls and correlation analyses investigated their relationship to memory performance. Results Memory performance was similar in SCI patients and healthy controls. The quality of the recorded MR spectra was excellent in comparison to the best-practice reports for the hippocampus. Metabolite concentrations and volume of the hippocampus measured based on MRS and MRI were not different between two groups. Memory performance in SCI patients and healthy controls was not correlated with metabolic or structural measures. Conclusion This study suggests that the hippocampus may not be pathologically affected at a functional, metabolic, and macrostructural level in chronic SCI. This points toward the absence of significant and clinically relevant trauma-induced neurodegeneration in the hippocampus.
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Affiliation(s)
- Dario Pfyffer
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Sandra Zimmermann
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland
| | - Kadir Şimşek
- Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Roland Kreis
- Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Patrick Freund
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Maryam Seif
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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13
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Shamaei AM, Starcukova J, Starcuk Z. Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data. Comput Biol Med 2023; 158:106837. [PMID: 37044049 DOI: 10.1016/j.compbiomed.2023.106837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 03/06/2023] [Accepted: 03/26/2023] [Indexed: 04/08/2023]
Abstract
PURPOSE While the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning requires ground truth fitted spectra, which is not practical. Moreover, this work investigates the feasibility and efficiency of the LCM-based self-supervised DL method for the analysis of MRS data. METHOD We present a novel DL-based method for the quantification of relative metabolite concentrations, using quantum-mechanics simulated metabolite responses and neural networks. We trained, validated, and evaluated the proposed networks with simulated and publicly accessible in-vivo human brain MRS data and compared the performance with traditional methods. A novel adaptive macromolecule fitting algorithm is included. We investigated the performance of the proposed methods in a Monte Carlo (MC) study. RESULT The validation using low-SNR simulated data demonstrated that the proposed methods could perform quantification comparably to other methods. The applicability of the proposed method for the quantification of in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. CONCLUSION The proposed model-constrained deep neural networks trained in a self-supervised manner can offer fast and efficient quantification of MRS and MRSI data. Our proposed method has the potential to facilitate clinical practice by enabling faster processing of large datasets such as high-resolution MRSI datasets, which may have thousands of spectra.
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14
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Tal A. The future is 2D: spectral-temporal fitting of dynamic MRS data provides exponential gains in precision over conventional approaches. Magn Reson Med 2023; 89:499-507. [PMID: 36121336 PMCID: PMC10087547 DOI: 10.1002/mrm.29456] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/01/2022] [Accepted: 08/23/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE Many MRS paradigms produce 2D spectral-temporal datasets, including diffusion-weighted, functional, and hyperpolarized and enriched (carbon-13, deuterium) experiments. Conventionally, temporal parameters-such as T2 , T1 , or diffusion constants-are assessed by first fitting each spectrum independently and subsequently fitting a temporal model (1D fitting). We investigated whether simultaneously fitting the entire dataset using a single spectral-temporal model (2D fitting) would improve the precision of the relevant temporal parameter. METHODS We derived a Cramer Rao lower bound for the temporal parameters for both 1D and 2D approaches for 2 experiments: a multi-echo experiment designed to estimate metabolite T2 s, and a functional MRS experiment designed to estimate fractional change ( δ $$ \delta $$ ) in metabolite concentrations. We investigated the dependence of the relative standard deviation (SD) of T2 in multi-echo and δ $$ \delta $$ in functional MRS. RESULTS When peaks were spectrally distant, 2D fitting improved precision by approximately 20% relative to 1D fitting, regardless of the experiment and other parameter values. These gains increased exponentially as peaks drew closer. Dependence on temporal model parameters was weak to negligible. CONCLUSION Our results strongly support a 2D approach to MRS fitting where applicable, and particularly in nuclei such as hydrogen and deuterium, which exhibit substantial spectral overlap.
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Affiliation(s)
- Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
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15
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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] [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,*Correspondence: Brenda Bartnik-Olson ✉
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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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Marjańska M, Deelchand DK, Kreis R. Results and interpretation of a fitting challenge for MR spectroscopy set up by the MRS study group of ISMRM. Magn Reson Med 2022; 87:11-32. [PMID: 34337767 PMCID: PMC8616800 DOI: 10.1002/mrm.28942] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/25/2021] [Accepted: 07/06/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE Fitting of MRS data plays an important role in the quantification of metabolite concentrations. Many different spectral fitting packages are used by the MRS community. A fitting challenge was set up to allow comparison of fitting methods on the basis of performance and robustness. METHODS Synthetic data were generated for 28 datasets. Short-echo time PRESS spectra were simulated using ideal pulses for the common metabolites at mostly near-normal brain concentrations. Macromolecular contributions were also included. Modulations of signal-to-noise ratio (SNR); lineshape type and width; concentrations of γ-aminobutyric acid, glutathione, and macromolecules; and inclusion of artifacts and lipid signals to mimic tumor spectra were included as challenges to be coped with. RESULTS Twenty-six submissions were evaluated. Visually, most fit packages performed well with mostly noise-like residuals. However, striking differences in fit performance were found with bias problems also evident for well-known packages. In addition, often error bounds were not appropriately estimated and deduced confidence limits misleading. Soft constraints as used in LCModel were found to substantially influence the fitting results and their dependence on SNR. CONCLUSIONS Substantial differences were found for accuracy and precision of fit results obtained by the multiple fit packages.
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Affiliation(s)
- Małgorzata Marjańska
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Dinesh K. Deelchand
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Roland Kreis
- Magnetic Resonance Methodology group of the University Institute for Diagnostic and Interventional Neuroradiology and the Department of Biomedical Research, University Bern, Switzerland
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18
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Borbath T, Murali-Manohar S, Dorst J, Wright AM, Henning A. ProFit-1D-A 1D fitting software and open-source validation data sets. Magn Reson Med 2021; 86:2910-2929. [PMID: 34390031 DOI: 10.1002/mrm.28941] [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: 01/05/2021] [Revised: 07/05/2021] [Accepted: 07/06/2021] [Indexed: 11/05/2022]
Abstract
PURPOSE Accurate and precise MRS fitting is crucial for metabolite concentration quantification of 1 H-MRS spectra. LCModel, a spectral fitting software, has shown to have certain limitations to perform advanced spectral fitting by previous literature. Herein, we propose an open-source spectral fitting algorithm with adaptive spectral baseline determination and more complex cost functions. THEORY The MRS spectra are characterized by several parameters, which reflect the environment of the contributing metabolites, properties of the acquisition sequence, or additional disturbances. Fitting parameters should accurately describe these parameters. Baselines are also a major contributor to MRS spectra, in which smoothness of the spline baselines used for fitting can be adjusted based on the properties of the spectra. Three different cost functions used for the minimization problem were also investigated. METHODS The newly developed ProFit-1D fitting algorithm is systematically evaluated for simulations of several types of possible in vivo parameter variations. Although accuracy and precision are tested with simulated spectra, spectra measured in vivo at 9.4 T are used for testing precision using subsets of averages. ProFit-1D fitting results are also compared with LCModel. RESULTS Both ProFit-1D and LCModel fitted the spectra well with induced parameter and baseline variations. ProFit-1D proved to be more accurate than LCModel for simulated spectra. However, LCModel showed a somewhat increased precision for some spectral simulations and for in vivo data. CONCLUSION The open-source ProFit-1D fitting algorithm demonstrated high accuracy while maintaining precise metabolite concentration quantification. Finally, through the newly proposed cost functions, new ways to improve fitting were shown.
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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
| | - Johanna Dorst
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive & Systems Neuroscience, 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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19
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Simicic D, Rackayova V, Xin L, Tkáč I, Borbath T, Starcuk Z, Starcukova J, Lanz B, Cudalbu C. In vivo macromolecule signals in rat brain 1 H-MR spectra at 9.4T: Parametrization, spline baseline estimation, and T 2 relaxation times. Magn Reson Med 2021; 86:2384-2401. [PMID: 34268821 PMCID: PMC8596437 DOI: 10.1002/mrm.28910] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/25/2021] [Accepted: 06/11/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE Reliable detection and fitting of macromolecules (MM) are crucial for accurate quantification of brain short-echo time (TE) 1 H-MR spectra. An experimentally acquired single MM spectrum is commonly used. Higher spectral resolution at ultra-high field (UHF) led to increased interest in using a parametrized MM spectrum together with flexible spline baselines to address unpredicted spectroscopic components. Herein, we aimed to: (1) implement an advanced methodological approach for post-processing, fitting, and parametrization of 9.4T rat brain MM spectra; (2) assess the concomitant impact of the LCModel baseline and MM model (ie, single vs parametrized); and (3) estimate the apparent T2 relaxation times for seven MM components. METHODS A single inversion recovery sequence combined with advanced AMARES prior knowledge was used to eliminate the metabolite residuals, fit, and parametrize 10 MM components directly from 9.4T rat brain in vivo 1 H-MR spectra at different TEs. Monte Carlo simulations were also used to assess the concomitant influence of parametrized MM and DKNTMN parameter in LCModel. RESULTS A very stiff baseline (DKNTMN ≥ 1 ppm) in combination with a single MM spectrum led to deviations in metabolite concentrations. For some metabolites the parametrized MM showed deviations from the ground truth for all DKNTMN values. Adding prior knowledge on parametrized MM improved MM and metabolite quantification. The apparent T2 ranged between 12 and 24 ms for seven MM peaks. CONCLUSION Moderate flexibility in the spline baseline was required for reliable quantification of real/experimental spectra based on in vivo and Monte Carlo data. Prior knowledge on parametrized MM improved MM and metabolite quantification.
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Affiliation(s)
- Dunja Simicic
- CIBM Center for Biomedical Imaging, Switzerland.,Animal Imaging and Technology, EPFL, Lausanne, Switzerland.,Laboratory for functional and metabolic imaging (LIFMET), EPFL, Lausanne, Switzerland
| | - Veronika Rackayova
- CIBM Center for Biomedical Imaging, Switzerland.,Animal Imaging and Technology, EPFL, Lausanne, Switzerland
| | - Lijing Xin
- CIBM Center for Biomedical Imaging, Switzerland.,Animal Imaging and Technology, EPFL, Lausanne, Switzerland
| | - Ivan Tkáč
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Tamas Borbath
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Zenon Starcuk
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic
| | - Jana Starcukova
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic
| | - Bernard Lanz
- Laboratory for functional and metabolic imaging (LIFMET), EPFL, Lausanne, Switzerland
| | - Cristina Cudalbu
- CIBM Center for Biomedical Imaging, Switzerland.,Animal Imaging and Technology, EPFL, Lausanne, Switzerland
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20
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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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21
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Kulpanovich A, Tal A. What is the optimal schedule for multiparametric MRS? A magnetic resonance fingerprinting perspective. NMR IN BIOMEDICINE 2021; 34:e4196. [PMID: 31814197 PMCID: PMC9244865 DOI: 10.1002/nbm.4196] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 09/09/2019] [Accepted: 09/09/2019] [Indexed: 05/09/2023]
Abstract
Clinical magnetic resonance spectroscopy (MRS) mainly concerns itself with the quantification of metabolite concentrations. Metabolite relaxation values, which reflect the microscopic state of specific cellular and sub-cellular environments, could potentially hold additional valuable information, but are rarely acquired within clinical scan times. By varying the flip angle, repetition time and echo time in a preset way (termed a schedule), and matching the resulting signals to a pre-generated dictionary - an approach dubbed magnetic resonance fingerprinting - it is possible to encode the spins' relaxation times into the acquired signal, simultaneously quantifying multiple tissue parameters for each metabolite. Herein, we optimized the schedule to minimize the averaged root mean square error (RMSE) across all estimated parameters: concentrations, longitudinal and transverse relaxation time, and transmitter inhomogeneity. The optimal schedules were validated in phantoms and, subsequently, in a cohort of healthy volunteers, in a 4.5 mL parietal white matter single voxel and an acquisition time under 5 minutes. The average intra-subject, inter-scan coefficients of variation (CVs) for metabolite concentrations, T1 and T2 relaxation times were found to be 3.4%, 4.6% and 4.7% in-vivo, respectively, averaged over all major singlets. Coupled metabolites were quantified using the short echo time schedule entries and spectral fitting, and reliable estimates of glutamate+glutamine, glutathione and myo-inositol were obtained.
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Affiliation(s)
- Alexey Kulpanovich
- Department of Chemical Physics, Weizmann Institute of Science, 234 Herzel St., Rehovot 7610001, Israel
| | - Assaf Tal
- Department of Chemical Physics, Weizmann Institute of Science, 234 Herzel St., Rehovot 7610001, Israel
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22
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Near J, Harris AD, Juchem C, Kreis R, Marjańska M, Öz G, Slotboom J, Wilson M, Gasparovic C. Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: experts' consensus recommendations. NMR IN BIOMEDICINE 2021; 34:e4257. [PMID: 32084297 PMCID: PMC7442593 DOI: 10.1002/nbm.4257] [Citation(s) in RCA: 153] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 12/21/2019] [Accepted: 12/22/2019] [Indexed: 05/05/2023]
Abstract
Once an MRS dataset has been acquired, several important steps must be taken to obtain the desired metabolite concentration measures. First, the data must be preprocessed to prepare them for analysis. Next, the intensity of the metabolite signal(s) of interest must be estimated. Finally, the measured metabolite signal intensities must be converted into scaled concentration units employing a quantitative reference signal to allow meaningful interpretation. In this paper, we review these three main steps in the post-acquisition workflow of a single-voxel MRS experiment (preprocessing, analysis and quantification) and provide recommendations for best practices at each step.
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Affiliation(s)
- Jamie Near
- Douglas Mental Health University Institute and Department of Psychiatry, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
| | - Ashley D. Harris
- Department of Radiology, University of Calgary, Calgary, Canada
- Alberta Children’s Hospital Research Institute, Calgary, Canada
- Hotchkiss Brain Institute, Calgary, Canada
| | - Christoph Juchem
- Department of Biomedical Engineering, Columbia University, New York NY, USA
| | - Roland Kreis
- Departments of Radiology and Biomedical Research, University Bern, Switzerland
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis MN, USA
| | - Gülin Öz
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis MN, USA
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging (SCAN), Neuroradiology, University Hospital Inselspital, Bern, Switzerland
| | - Martin Wilson
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, England
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23
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Hoefemann M, Döring A, Fichtner ND, Kreis R. Combining chemical exchange saturation transfer and 1 H magnetic resonance spectroscopy for simultaneous determination of metabolite concentrations and effects of magnetization exchange. Magn Reson Med 2020; 85:1766-1782. [PMID: 33151011 PMCID: PMC7821128 DOI: 10.1002/mrm.28574] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 10/06/2020] [Accepted: 10/07/2020] [Indexed: 12/21/2022]
Abstract
Purpose A new sequence combining chemical‐exchange saturation‐transfer (CEST) with traditional MRS is used to simultaneously determine metabolite content and effects of magnetization exchange. Methods A CEST saturation block consisting of a train of RF‐pulses is placed before a metabolite‐cycled semi‐LASER single‐voxel spectroscopy sequence. The saturation parameters are adjustable to allow optimization of the saturation for a specific target. Data were collected in brain from 20 subjects in experiments with different B1‐settings (0.4‐2.0 µT) on a 3T MR scanner. CEST Z‐spectra were calculated from water intensities and fitted with a multi‐pool Lorentzian model. Interrelated metabolite spectra were fitted in fitting tool for arrays of interrelated datasets (FiTAID). Results Evaluation of traditional Z‐spectra from water revealed exchange effects from amides, amines, and hydroxyls as well as an upfield nuclear Overhauser effect. The magnetization transfer effect was evaluated on metabolites and macromolecules for the whole spectral range and for the different B1 levels. A correction scheme for direct saturation on metabolites is proposed. Both magnetization‐transfer and direct saturation proved to differ for individual metabolites. Conclusion Using non‐water‐suppressed spectroscopy offers time‐saving simultaneous recording of the traditional CEST Z‐spectrum from water and the metabolite spectrum under frequency‐selective saturation. In addition, exchange and magnetization‐transfer effects on metabolites and macromolecules can be detected, which might offer additional possibilities for quantification or give further insight into the composition of the traditional CEST Z‐spectrum. Apparent magnetization‐transfer effects on macromolecular signals in the 1H‐MR spectrum have been found. Detailed knowledge of magnetization‐transfer effects is also relevant for judging the influence of water‐suppression on the quantification of metabolite signals.
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Affiliation(s)
- Maike Hoefemann
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - André Döring
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland.,Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Nicole Damara Fichtner
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland.,University of British Columbia, Vancouver, Canada
| | - Roland Kreis
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland
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24
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Hoefemann M, Bolliger CS, Chong DGQ, van der Veen JW, Kreis R. Parameterization of metabolite and macromolecule contributions in interrelated MR spectra of human brain using multidimensional modeling. NMR IN BIOMEDICINE 2020; 33:e4328. [PMID: 32542861 DOI: 10.1002/nbm.4328] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 05/07/2020] [Accepted: 05/08/2020] [Indexed: 06/11/2023]
Abstract
Macromolecular signals are crucial constituents of short echo-time 1 H MR spectra with potential clinical implications in themselves as well as essential ramifications for the quantification of the usually targeted metabolites. Their parameterization, needed for general fitting models, is difficult because of their unknown composition. Here, a macromolecular signal parameterization together with metabolite signal quantification including relaxation properties is investigated by multidimensional modeling of interrelated 2DJ inversion-recovery (2DJ-IR) datasets. Simultaneous and iterative procedures for defining the macromolecular background (MMBG) as mono-exponentially or generally decaying signals over TE are evaluated. Varying prior knowledge and restrictions in the metabolite evaluation are tested to examine their impact on results and fitting stability for two sets of three-dimensional spectra acquired with metabolite-cycled PRESS from cerebral gray and white matter locations. One dataset was used for model optimization, and also examining the influence of prior knowledge on estimated parameters. The most promising model was applied to a second dataset. It turned out that the mono-exponential decay model appears to be inadequate to represent TE-dependent signal features of the MMBG. TE-adapted MMBG spectra were therefore determined. For a reliable overall quantification of implicated metabolite concentrations and relaxation times, a general fitting model had to be constrained in terms of the number of fitting variables and the allowed parameter space. With such a model in place, fitting precision for metabolite contents and relaxation times was excellent, while fitting accuracy is difficult to judge and bias was likely influenced by the type of fitting constraints enforced. In summary, the parameterization of metabolite and macromolecule contributions in interrelated MR spectra has been examined by using multidimensional modeling on complex 2DJ-IR datasets. A tightly restricted model allows fitting of individual subject data with high fitting precision documented in small Cramér-Rao lower bounds, good repeatability values and a relatively small spread of estimated concentration and relaxation values for a healthy subject cohort.
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Affiliation(s)
- Maike Hoefemann
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Christine Sandra Bolliger
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland
- Bruker BioSpin AG, Fällanden, Switzerland
| | - Daniel G Q Chong
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | | | - Roland Kreis
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland
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25
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Kreis R, Boer V, Choi I, Cudalbu C, de Graaf RA, Gasparovic C, Heerschap A, Krššák M, Lanz B, Maudsley AA, Meyerspeer M, Near J, Öz G, Posse S, Slotboom J, Terpstra M, Tkáč I, Wilson M, Bogner W. Terminology and concepts for the characterization of in vivo MR spectroscopy methods and MR spectra: Background and experts' consensus recommendations. NMR IN BIOMEDICINE 2020; 34:e4347. [PMID: 32808407 PMCID: PMC7887137 DOI: 10.1002/nbm.4347] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 05/20/2020] [Accepted: 05/21/2020] [Indexed: 05/04/2023]
Abstract
With a 40-year history of use for in vivo studies, the terminology used to describe the methodology and results of magnetic resonance spectroscopy (MRS) has grown substantially and is not consistent in many aspects. Given the platform offered by this special issue on advanced MRS methodology, the authors decided to describe many of the implicated terms, to pinpoint differences in their meanings and to suggest specific uses or definitions. This work covers terms used to describe all aspects of MRS, starting from the description of the MR signal and its theoretical basis to acquisition methods, processing and to quantification procedures, as well as terms involved in describing results, for example, those used with regard to aspects of quality, reproducibility or indications of error. The descriptions of the meanings of such terms emerge from the descriptions of the basic concepts involved in MRS methods and examinations. This paper also includes specific suggestions for future use of terms where multiple conventions have emerged or coexisted in the past.
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Affiliation(s)
- Roland Kreis
- Department of Radiology, Neuroradiology, and Nuclear Medicine and Department of Biomedical ResearchUniversity BernBernSwitzerland
| | - Vincent Boer
- Danish Research Centre for Magnetic Resonance, Funktions‐ og Billeddiagnostisk EnhedCopenhagen University Hospital HvidovreHvidovreDenmark
| | - In‐Young Choi
- Department of Neurology, Hoglund Brain Imaging CenterUniversity of Kansas Medical CenterKansas CityKansasUSA
| | - Cristina Cudalbu
- Centre d'Imagerie Biomedicale (CIBM)Ecole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Robin A. de Graaf
- Department of Radiology and Biomedical Imaging & Department of Biomedical EngineeringYale UniversityNew HavenConnecticutUSA
| | | | - Arend Heerschap
- Department of Radiology and Nuclear MedicineRadboud University Medical CenterNijmegenThe Netherlands
| | - Martin Krššák
- Division of Endocrinology and Metabolism, Department of Internal Medicine III & High Field MR Centre, Department of Biomedical Imaging and Image guided TherapyMedical University of ViennaViennaAustria
| | - Bernard Lanz
- Laboratory of Functional and Metabolic Imaging (LIFMET)Ecole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
| | - Andrew A. Maudsley
- Department of Radiology, Miller School of MedicineUniversity of MiamiMiamiFloridaUSA
| | - Martin Meyerspeer
- Center for Medical Physics and Biomedical EngineeringMedical University of ViennaViennaAustria
- High Field MR CenterMedical University of ViennaViennaAustria
| | - Jamie Near
- Douglas Mental Health University Institute and Department of PsychiatryMcGill UniversityMontrealCanada
| | - Gülin Öz
- Center for Magnetic Resonance Research, Department of RadiologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Stefan Posse
- Department of NeurologyUniversity of New Mexico School of MedicineAlbuquerqueNew MexicoUSA
| | - Johannes Slotboom
- Department of Radiology, Neuroradiology, and Nuclear MedicineUniversity Hospital BernBernSwitzerland
| | - Melissa Terpstra
- Center for Magnetic Resonance Research, Department of RadiologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Ivan Tkáč
- Center for Magnetic Resonance Research, Department of RadiologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Martin Wilson
- Centre for Human Brain Health and School of PsychologyUniversity of BirminghamBirminghamUK
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
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26
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Meienberg F, Loher H, Bucher J, Jenni S, Krüsi M, Kreis R, Boesch C, Betz MJ, Christ E. The effect of exercise on intramyocellular acetylcarnitine (AcCtn) concentration in adult growth hormone deficiency (GHD). Sci Rep 2019; 9:19431. [PMID: 31857652 PMCID: PMC6923484 DOI: 10.1038/s41598-019-55942-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 11/13/2019] [Indexed: 01/28/2023] Open
Abstract
To cover increasing energy demands during exercise, tricarboxylic cycle (TCA) flux in skeletal muscle is markedly increased, resulting in the increased formation of intramyocellular acetylcarnitine (AcCtn). We hypothesized that reduced substrate availability within the exercising muscle, reflected by a diminished increase of intramyocellular AcCtn concentration during exercise, might be an underlying mechanism for the impaired exercise performance observed in adult patients with growth hormone deficiency (GHD). We aimed at assessing the effect of 2 hours of moderately intense exercise on intramyocellular AcCtn concentrations, measured by proton magnetic resonance spectroscopy (1H-MRS), in seven adults with GHD compared to seven matched control subjects (CS). Compared to baseline levels AcCtn concentrations significantly increased after 2 hours of exercise, and significantly decreased over the following 24 hours (ANOVA p for effect of time = 0.0023 for all study participants; p = 0.067 for GHD only, p = 0.045 for CS only). AcCtn concentrations at baseline, as well as changes in AcCtn concentrations over time were similar between GHD patients and CS (ANOVA p for group effect = 0.45). There was no interaction between group and time (p = 0.53). Our study suggests that during moderately intense exercise the availability of energy substrate within the exercising muscle is not significantly different in GHD patients compared to CS.
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Affiliation(s)
- Fabian Meienberg
- Endocrinology & Diabetology, Kantonsspital Baselland, Liestal, Switzerland
| | - Hannah Loher
- Innere Medizin, Kantonsspital, St. Gallen, Switzerland
| | | | - Stefan Jenni
- Praxis Endokrinologie Diabetologie Bern, Bern, Switzerland
| | - Marion Krüsi
- Praxis Endokrinologie & Diabetologie, Zürich Unterland, Embrach, Switzerland
| | - Roland Kreis
- Departments of Biomedical Research and Radiology, University Bern, Bern, Switzerland
| | - Chris Boesch
- Departments of Biomedical Research and Radiology, University Bern, Bern, Switzerland
| | - Matthias Johannes Betz
- Endocrinology, Diabetes & Metabolism, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Emanuel Christ
- Endocrinology, Diabetes & Metabolism, University Hospital Basel and University of Basel, Basel, Switzerland.
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Magnetic resonance spectroscopy extended by oscillating diffusion gradients: Cell-specific anomalous diffusion as a probe for tissue microstructure in human brain. Neuroimage 2019; 202:116075. [DOI: 10.1016/j.neuroimage.2019.116075] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 08/01/2019] [Accepted: 08/04/2019] [Indexed: 11/23/2022] Open
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Hoefemann M, Adalid V, Kreis R. Optimizing acquisition and fitting conditions for 1 H MR spectroscopy investigations in global brain pathology. NMR IN BIOMEDICINE 2019; 32:e4161. [PMID: 31410911 DOI: 10.1002/nbm.4161] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 06/18/2019] [Accepted: 06/19/2019] [Indexed: 05/23/2023]
Abstract
PURPOSE To optimize acquisition and fitting conditions for nonfocal disease in terms of voxel size and use of individual coil element data. Increasing the voxel size yields a higher signal-to-noise ratio, but leads to larger linewidths and more artifacts. Several ways to improve the spectral quality for large voxels are exploited and the optimal use of individual coil signals investigated. METHODS Ten human subjects were measured at 3 T using a 64-channel receive head coil with a semi-LASER localization sequence under optimized and deliberately mis-set field homogeneity. Eight different voxel sizes (8 to 99 cm3 ) were probed. Spectra were fitted either as weighted sums of the individual coil elements or simultaneously without summation. Eighteen metabolites were included in the fit model that also included the lineshapes from all coil elements as reflected in water reference data. Fitting errors for creatine, myo-Inositol and glutamate are reported as representative parameters to judge optimal acquisition and evaluation conditions. RESULTS Minimal Cramér-Rao lower bounds and thus optimal acquisition conditions were found for a voxel size of ~ 70 cm3 for the representative upfield metabolites. Spectral quality in terms of lineshape and artifact appearance was determined to differ substantially between coil elements. Simultaneous fitting of spectra from individual coil elements instead of traditional fitting of a weighted sum spectrum reduced Cramer-Rao lower bounds by up to 17% for large voxel sizes. CONCLUSION The optimal voxel size for best precision in determined metabolite content is surprisingly large. Such an acquisition condition is most relevant for detection of low-concentration metabolites, like NAD+ or phenylalanine, but also for longitudinal studies where very small alterations in metabolite content are targeted. In addition, simultaneous fitting of single channel spectra enforcing lineshape and coil sensitivity information proved to be superior to traditional signal combination with subsequent fitting.
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Affiliation(s)
- Maike Hoefemann
- Depts. Radiology and Biomedical Research, University of Bern, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Victor Adalid
- Depts. Radiology and Biomedical Research, University of Bern, Bern, Switzerland
| | - Roland Kreis
- Depts. Radiology and Biomedical Research, University of Bern, Bern, Switzerland
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Landheer K, Schulte RF, Treacy MS, Swanberg KM, Juchem C. Theoretical description of modern1H in Vivo magnetic resonance spectroscopic pulse sequences. J Magn Reson Imaging 2019; 51:1008-1029. [DOI: 10.1002/jmri.26846] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 06/14/2019] [Accepted: 06/17/2019] [Indexed: 01/20/2023] Open
Affiliation(s)
- Karl Landheer
- Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science New York New York USA
| | | | - Michael S. Treacy
- Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science New York New York USA
| | - Kelley M. Swanberg
- Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science New York New York USA
| | - Christoph Juchem
- Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science New York New York USA
- Radiology, Columbia University College of Physicians and Surgeons New York New York USA
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Moser P, Hingerl L, Strasser B, Považan M, Hangel G, Andronesi OC, van der Kouwe A, Gruber S, Trattnig S, Bogner W. Whole-slice mapping of GABA and GABA + at 7T via adiabatic MEGA-editing, real-time instability correction, and concentric circle readout. Neuroimage 2019; 184:475-489. [PMID: 30243974 PMCID: PMC7212034 DOI: 10.1016/j.neuroimage.2018.09.039] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 08/20/2018] [Accepted: 09/15/2018] [Indexed: 01/29/2023] Open
Abstract
An adiabatic MEscher-GArwood (MEGA)-editing scheme, using asymmetric hyperbolic secant editing pulses, was developed and implemented in a B1+-insensitive, 1D-semiLASER (Localization by Adiabatic SElective Refocusing) MR spectroscopic imaging (MRSI) sequence for the non-invasive mapping of γ-aminobutyric acid (GABA) over a whole brain slice. Our approach exploits the advantages of edited-MRSI at 7T while tackling challenges that arise with ultra-high-field-scans. Spatial-spectral encoding, using density-weighted, concentric circle echo planar trajectory readout, enabled substantial MRSI acceleration and an improved point-spread-function, thereby reducing extracranial lipid signals. Subject motion and scanner instabilities were corrected in real-time using volumetric navigators optimized for 7T, in combination with selective reacquisition of corrupted data to ensure robust subtraction-based MEGA-editing. Simulations and phantom measurements of the adiabatic MEGA-editing scheme demonstrated stable editing efficiency even in the presence of ±0.15 ppm editing frequency offsets and B1+ variations of up to ±30% (as typically encountered in vivo at 7T), in contrast to conventional Gaussian editing pulses. Volunteer measurements were performed with and without global inversion recovery (IR) to study regional GABA levels and their underlying, co-edited, macromolecular (MM) signals at 2.99 ppm. High-quality in vivo spectra allowed mapping of pure GABA and MM-contaminated GABA+ (GABA + MM) along with Glx (Glu + Gln), with high-resolution (eff. voxel size: 1.4 cm3) and whole-slice coverage in 24 min scan time. Metabolic ratio maps of GABA/tNAA, GABA+/tNAA, and Glx/tNAA were correlated linearly with the gray matter fraction of each voxel. A 2.15-fold increase in gray matter to white matter contrast was observed for GABA when enabling IR, which we attribute to the higher abundance of macromolecules at 2.99 ppm in the white matter than in the gray matter. In conclusion, adiabatic MEGA-editing with 1D-semiLASER selection is as a promising approach for edited-MRSI at 7T. Our sequence capitalizes on the benefits of ultra-high-field MRSI while successfully mitigating the challenges related to B0/B1+ inhomogeneities, prolonged scan times, and motion/scanner instability artifacts. Robust and accurate 2D mapping has been shown for the neurotransmitters GABA and Glx.
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Affiliation(s)
- Philipp Moser
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; Christian Doppler Laboratory for Clinical Molecular MRI, Vienna, Austria.
| | - Lukas Hingerl
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria.
| | - Bernhard Strasser
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Michal Považan
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Gilbert Hangel
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria.
| | - Ovidiu C Andronesi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Stephan Gruber
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria.
| | - Siegfried Trattnig
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria.
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria.
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Cros J, Pianezzi E, Rosset R, Egli L, Schneiter P, Cornette F, Pouymayou B, Heinzer R, Tappy L, Kreis R, Boesch C, Haba-Rubio J, Lecoultre V. Impact of sleep restriction on metabolic outcomes induced by overfeeding: a randomized controlled trial in healthy individuals. Am J Clin Nutr 2019; 109:17-28. [PMID: 30615104 DOI: 10.1093/ajcn/nqy215] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 08/06/2018] [Indexed: 01/03/2023] Open
Abstract
Background Overconsumption of energy-dense foods and sleep restriction are both associated with the development of metabolic and cardiovascular diseases, but their combined effects remain poorly evaluated. Objective The aim of this study was to assess whether sleep restriction potentiates the effects of a short-term overfeeding on intrahepatocellular lipid (IHCL) concentrations and on glucose homeostasis. Design Ten healthy subjects were exposed to a 6-d overfeeding period (130% daily energy needs, with 15% extra energy as sucrose and 15% as fat), with normal sleep (8 h sleep opportunity time) or sleep restriction (4 h sleep opportunity time), according to a randomized, crossover design. At baseline and after intervention, IHCL concentrations were measured by proton magnetic resonance spectroscopy, and a dual intravenous [6,6-2H2]-, oral 13C-labeled glucose tolerance test and a polysomnographic recording were performed. Results Overfeeding significantly increased IHCL concentrations (Poverfeeding < 0.001; overfeeding + normal sleep: +53% ± 16%). During the oral glucose tolerance test, overfeeding significantly increased endogenous glucose production (Poverfeeding = 0.034) and the oxidation of 13C-labeled glucose load (Poverfeeding = 0.038). Sleep restriction significantly decreased total sleep time, and the duration of stages 1 and 2 and rapid eye movement sleep (all P < 0.001), whereas slow-wave sleep duration was preserved (Poverfeeding × sleep = 0.809). Compared with overfeeding, overfeeding + sleep restriction did not change IHCL concentrations (Poverfeeding × sleep = 0.541; +83% ± 33%), endogenous glucose production (Poverfeeding × sleep = 0.567), or exogenous glucose oxidation (Poverfeeding × sleep = 0.118). Sleep restriction did not significantly alter blood pressure, heart rate, or plasma cortisol concentrations (all Poverfeeding × sleep = NS). Conclusions Six days of a high-sucrose, high-fat overfeeding diet significantly increased IHCL concentrations and increased endogenous glucose production, suggesting hepatic insulin resistance. These effects of overfeeding were not altered by sleep restriction. This trial was registered at clinicaltrials.gov as NCT02075723. Other study ID numbers: SleepDep 02/14.
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Affiliation(s)
- Jérémy Cros
- Department of Physiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Enea Pianezzi
- Department of Physiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Robin Rosset
- Department of Physiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Léonie Egli
- Department of Physiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Philippe Schneiter
- Department of Physiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Françoise Cornette
- Center for Investigation and Research in Sleep, Lausanne University Hospital, Faculty of Biology and Medicine, Lausanne, Switzerland
| | - Bertrand Pouymayou
- Department of Biomedical Research and Department of Radiology, University of Bern, Bern, Switzerland
| | - Raphaël Heinzer
- Center for Investigation and Research in Sleep, Lausanne University Hospital, Faculty of Biology and Medicine, Lausanne, Switzerland
| | - Luc Tappy
- Department of Physiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.,Cardio-Metabolic Center, Broye Hospital, Estavayer-le-lac, Switzerland
| | - Roland Kreis
- Department of Biomedical Research and Department of Radiology, University of Bern, Bern, Switzerland
| | - Chris Boesch
- Department of Biomedical Research and Department of Radiology, University of Bern, Bern, Switzerland
| | - José Haba-Rubio
- Center for Investigation and Research in Sleep, Lausanne University Hospital, Faculty of Biology and Medicine, Lausanne, Switzerland
| | - Virgile Lecoultre
- Department of Physiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.,Cardio-Metabolic Center, Broye Hospital, Estavayer-le-lac, Switzerland
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32
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Döring A, Adalid V, Boesch C, Kreis R. Diffusion-weighted magnetic resonance spectroscopy boosted by simultaneously acquired water reference signals. Magn Reson Med 2018; 80:2326-2338. [DOI: 10.1002/mrm.27222] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 03/19/2018] [Accepted: 03/27/2018] [Indexed: 11/08/2022]
Affiliation(s)
- André Döring
- Departments of Radiology and Biomedical Research; University of Bern; Bern Switzerland
- Graduate School for Cellular and Biomedical Sciences; University of Bern; Bern Switzerland
| | - Victor Adalid
- Departments of Radiology and Biomedical Research; University of Bern; Bern Switzerland
- Graduate School for Cellular and Biomedical Sciences; University of Bern; Bern Switzerland
| | - Chris Boesch
- Departments of Radiology and Biomedical Research; University of Bern; Bern Switzerland
| | - Roland Kreis
- Departments of Radiology and Biomedical Research; University of Bern; Bern Switzerland
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Giapitzakis IA, Avdievich N, Henning A. Characterization of macromolecular baseline of human brain using metabolite cycled semi-LASER at 9.4T. Magn Reson Med 2018; 80:462-473. [DOI: 10.1002/mrm.27070] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 12/12/2017] [Accepted: 12/12/2017] [Indexed: 11/11/2022]
Affiliation(s)
- Ioannis-Angelos Giapitzakis
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics; Tübingen Germany
- IMPRS for Cognitive & Systems Neuroscience; 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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34
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Fichtner ND, Giapitzakis IA, Avdievich N, Mekle R, Zaldivar D, Henning A, Kreis R. In vivo characterization of the downfield part of1H MR spectra of human brain at 9.4 T: Magnetization exchange with water and relation to conventionally determined metabolite content. Magn Reson Med 2017; 79:2863-2873. [DOI: 10.1002/mrm.26968] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 09/12/2017] [Accepted: 09/22/2017] [Indexed: 02/04/2023]
Affiliation(s)
- Nicole D. Fichtner
- Department of Radiology, Neuroradiology, and Nuclear Medicine; University of Bern; Bern Switzerland
- Department for BioMedical Research; University of Bern; Bern Switzerland
- Graduate School for Cellular and Biomedical Sciences; University of Bern; Bern Switzerland
- Institute for Biomedical Engineering, UZH and ETH Zurich; Zurich Switzerland
| | - Ioannis-Angelos Giapitzakis
- Max Planck Institute for Biological Cybernetics; Tübingen Germany
- Graduate School of Neural and Behavioural Sciences; Tübingen Germany
| | | | - Ralf Mekle
- Center for Stroke Research Berlin (CSB); Charité Universitätsmedizin Berlin; Berlin Germany
| | - Daniel Zaldivar
- Max Planck Institute for Biological Cybernetics; Tübingen Germany
| | - Anke Henning
- Max Planck Institute for Biological Cybernetics; Tübingen Germany
- Institute of Physics; Ernst-Moritz Arndt University Greifswald; Greifswald Germany
| | - Roland Kreis
- Department of Radiology, Neuroradiology, and Nuclear Medicine; University of Bern; Bern Switzerland
- Department for BioMedical Research; University of Bern; Bern Switzerland
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35
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Macromolecule mapping of the brain using ultrashort‐TE acquisition and reference‐based metabolite removal. Magn Reson Med 2017; 79:2460-2469. [DOI: 10.1002/mrm.26896] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 08/07/2017] [Accepted: 08/10/2017] [Indexed: 12/24/2022]
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Iqbal Z, Wilson NE, Thomas MA. Prior-knowledge Fitting of Accelerated Five-dimensional Echo Planar J-resolved Spectroscopic Imaging: Effect of Nonlinear Reconstruction on Quantitation. Sci Rep 2017; 7:6262. [PMID: 28740202 PMCID: PMC5524913 DOI: 10.1038/s41598-017-04065-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Accepted: 05/08/2017] [Indexed: 11/14/2022] Open
Abstract
1H Magnetic Resonance Spectroscopic imaging (SI) is a powerful tool capable of investigating metabolism in vivo from mul- tiple regions. However, SI techniques are time consuming, and are therefore difficult to implement clinically. By applying non-uniform sampling (NUS) and compressed sensing (CS) reconstruction, it is possible to accelerate these scans while re- taining key spectral information. One recently developed method that utilizes this type of acceleration is the five-dimensional echo planar J-resolved spectroscopic imaging (5D EP-JRESI) sequence, which is capable of obtaining two-dimensional (2D) spectra from three spatial dimensions. The prior-knowledge fitting (ProFit) algorithm is typically used to quantify 2D spectra in vivo, however the effects of NUS and CS reconstruction on the quantitation results are unknown. This study utilized a simulated brain phantom to investigate the errors introduced through the acceleration methods. Errors (normalized root mean square error >15%) were found between metabolite concentrations after twelve-fold acceleration for several low concentra- tion (<2 mM) metabolites. The Cramér Rao lower bound% (CRLB%) values, which are typically used for quality control, were not reflective of the increased quantitation error arising from acceleration. Finally, occipital white (OWM) and gray (OGM) human brain matter were quantified in vivo using the 5D EP-JRESI sequence with eight-fold acceleration.
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Affiliation(s)
- Zohaib Iqbal
- University of California - Los Angeles, Radiological Sciences, Los Angeles, California, 90095, USA
| | - Neil E Wilson
- University of California - Los Angeles, Radiological Sciences, Los Angeles, California, 90095, USA
| | - M Albert Thomas
- University of California - Los Angeles, Radiological Sciences, Los Angeles, California, 90095, USA.
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Schubert F, Kühn S, Gallinat J, Mekle R, Ittermann B. Towards a neurochemical profile of the amygdala using short-TE 1 H magnetic resonance spectroscopy at 3 T. NMR IN BIOMEDICINE 2017; 30:e3685. [PMID: 28058747 DOI: 10.1002/nbm.3685] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Revised: 10/21/2016] [Accepted: 11/18/2016] [Indexed: 06/06/2023]
Abstract
The amygdala plays a key role in emotional learning and in the processing of emotions. As disturbed amygdala function has been linked to several psychiatric conditions, a knowledge of its biochemistry, especially neurotransmitter levels, is highly desirable. The spin echo full intensity acquired localized (SPECIAL) sequence, together with a transmit/receive coil, was used to perform very short-TE magnetic resonance spectroscopy at 3 T to determine the neurochemical profile in a spectroscopic voxel containing the amygdala in 21 healthy adult subjects. For spectral analysis, advanced data processing was applied in combination with a macromolecule baseline measured in the anterior cingulate for spectral fitting. The concentrations of total N-acetylaspartate, total creatine, total choline, myo-inositol and, for the first time, glutamate were quantified with high reliability (uncertainties far below 10%). For these metabolites, the inter-individual variability, reflected by the relative standard deviations for the cohort studied, varied between 12% (glutamate) and 22% (myo-inositol). Glutamine and glutathione could also be determined, albeit with lower precision. Retest on four subjects showed good reproducibility. The devised method allows the determination of metabolite concentrations in the amygdala voxel, including glutamate, provides an estimation of glutamine and glutathione, and may help in the study of disturbed amygdala metabolism in pathologies such as anxiety disorder, autism and major depression.
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Affiliation(s)
- Florian Schubert
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Simone Kühn
- Max Planck Institute for Human Development, Center for Lifespan Psychology, Berlin, Germany
- Universitätsklinikum Hamburg-Eppendorf, Department of Psychiatry and Psychotherapy, Hamburg, Germany
| | - Jürgen Gallinat
- Universitätsklinikum Hamburg-Eppendorf, Department of Psychiatry and Psychotherapy, Hamburg, Germany
| | - Ralf Mekle
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Charité-Universitätsmedizin Berlin, Center for Stroke Research Berlin (CSB), Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
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Adalid V, Döring A, Kyathanahally SP, Bolliger CS, Boesch C, Kreis R. Fitting interrelated datasets: metabolite diffusion and general lineshapes. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2017; 30:429-448. [DOI: 10.1007/s10334-017-0618-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 03/24/2017] [Accepted: 03/27/2017] [Indexed: 12/23/2022]
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Lee HH, Kim H. Parameterization of spectral baseline directly from short echo time full spectra in 1
H-MRS. Magn Reson Med 2016; 78:836-847. [DOI: 10.1002/mrm.26502] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Revised: 08/11/2016] [Accepted: 09/18/2016] [Indexed: 12/24/2022]
Affiliation(s)
- Hyeong Hun Lee
- Department of Biomedical Sciences; Seoul National University; Seoul Korea
| | - Hyeonjin Kim
- Department of Biomedical Sciences; Seoul National University; Seoul Korea
- Department of Radiology; Seoul National University Hospital; Seoul Korea
- Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology; Seoul National University; Suwon Korea
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40
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Fichtner ND, Henning A, Zoelch N, Boesch C, Kreis R. Elucidation of the downfield spectrum of human brain at 7 T using multiple inversion recovery delays and echo times. Magn Reson Med 2016; 78:11-19. [DOI: 10.1002/mrm.26343] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Revised: 06/22/2016] [Accepted: 06/22/2016] [Indexed: 01/02/2023]
Affiliation(s)
- Nicole D. Fichtner
- Department of Radiology, Neuroradiology, and Nuclear Medicine; University of Bern; Bern Switzerland
- Department of Clinical Research; University of Bern; Bern Switzerland
- Graduate School for Cellular and Biomedical Sciences; University of Bern; Bern Switzerland
- Institute for Biomedical Engineering, UZH and ETH Zurich; Zurich Switzerland
| | - Anke Henning
- Institute for Biomedical Engineering, UZH and ETH Zurich; Zurich Switzerland
- Max Planck Institute for Biological Cybernetics; Tübingen Germany
| | - Niklaus Zoelch
- Institute for Biomedical Engineering, UZH and ETH Zurich; Zurich Switzerland
| | - Chris Boesch
- Department of Radiology, Neuroradiology, and Nuclear Medicine; University of Bern; Bern Switzerland
- Department of Clinical Research; University of Bern; Bern Switzerland
| | - Roland Kreis
- Department of Radiology, Neuroradiology, and Nuclear Medicine; University of Bern; Bern Switzerland
- Department of Clinical Research; University of Bern; Bern Switzerland
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41
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Pouymayou B, Buehler T, Kreis R, Boesch C. Test-retest analysis of multiple 31 P magnetization exchange pathways using asymmetric adiabatic inversion. Magn Reson Med 2016; 78:33-39. [PMID: 27455454 DOI: 10.1002/mrm.26337] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Revised: 05/31/2016] [Accepted: 06/17/2016] [Indexed: 01/08/2023]
Abstract
PURPOSE A 31 P-MR inversion transfer (IT) method with a short adiabatic inversion pulse is proposed and its test-retest reliability was evaluated for two spectral fitting strategies. METHODS Assessment in a test-retest design (3 Tesla, vastus muscles, 12 healthy volunteers, 14 inversion times, 22 ms asymmetric adiabatic inversion pulse, adiabatic excitation); spectral fitting in Fitting Tool for Interrelated Arrays of Datasets (FitAID) and Java Magnetic Resonance User Interface (jMRUI); least squares solution of the Bloch-McConnell-Solomon matrix formalism including all 14 measured time-points with equal weighting. RESULTS The cohort averages of k[PCr→γ-ATP] (phosphocreatine, PCr; adenosine triphosphate, ATP) are 0.246 ± 0.050s-1 versus 0.254 ± 0.050s-1 , and k[Pi→γ-ATP] 0.086 ± 0.033s-1 versus 0.066 ± 0.034s-1 (average ± standard deviation, jMRUI versus FitAID). Coefficients of variation of the differences between test and retest are lowest (9.5%) for k[PCr→γ-ATP] fitted in FitAID, larger (15.2%) for the fit in jMRUI, and considerably larger for k[Pi→γ-ATP] fitted in FitAID (43.4%) or jMRUI (47.9%). The beginning of the IT effect can be observed with magnetizations above 92% for noninverted lines while inversion of the ATP resonances is better than -72%. CONCLUSION The performance of the asymmetric adiabatic pulse allows an accurate observation of IT effects even in the early phase; the least squares fit of the Bloch-McConnell-Solomon matrix formalism is robust; and the type of spectral fitting can influence the results significantly. Magn Reson Med 78:33-39, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Bertrand Pouymayou
- Department of Clinical Research and Department of Radiology, University of Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Tania Buehler
- Department of Clinical Research and Department of Radiology, University of Bern, Switzerland
| | - Roland Kreis
- Department of Clinical Research and Department of Radiology, University of Bern, Switzerland
| | - Chris Boesch
- Department of Clinical Research and Department of Radiology, University of Bern, Switzerland
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42
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Iqbal Z, Wilson NE, Thomas MA. 3D spatially encoded and accelerated TE-averaged echo planar spectroscopic imaging in healthy human brain. NMR IN BIOMEDICINE 2016; 29:329-339. [PMID: 26748673 DOI: 10.1002/nbm.3469] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 11/17/2015] [Accepted: 11/23/2015] [Indexed: 06/05/2023]
Abstract
Several different pathologies, including many neurodegenerative disorders, affect the energy metabolism of the brain. Glutamate, a neurotransmitter in the brain, can be used as a biomarker to monitor these metabolic processes. One method that is capable of quantifying glutamate concentration reliably in several regions of the brain is TE-averaged (1) H spectroscopic imaging. However, this type of method requires the acquisition of multiple TE lines, resulting in long scan durations. The goal of this experiment was to use non-uniform sampling, compressed sensing reconstruction and an echo planar readout gradient to reduce the scan time by a factor of eight to acquire TE-averaged spectra in three spatial dimensions. Simulation of glutamate and glutamine showed that the 2.2-2.4 ppm spectral region contained 95% glutamate signal using the TE-averaged method. Peak integration of this spectral range and home-developed, prior-knowledge-based fitting were used for quantitation. Gray matter brain phantom measurements were acquired on a Siemens 3 T Trio scanner. Non-uniform sampling was applied retrospectively to these phantom measurements and quantitative results of glutamate with respect to creatine 3.0 (Glu/Cr) ratios showed a coefficient of variance of 16% for peak integration and 9% for peak fitting using eight-fold acceleration. In vivo scans of the human brain were acquired as well and five different brain regions were quantified using the prior-knowledge-based algorithm. Glu/Cr ratios from these regions agreed with previously reported results in the literature. The method described here, called accelerated TE-averaged echo planar spectroscopic imaging (TEA-EPSI), is a significant methodological advancement and may be a useful tool for categorizing glutamate changes in pathologies where affected brain regions are not known a priori. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Zohaib Iqbal
- Department of Radiological Sciences, University of California Los Angeles, USA
| | - Neil E Wilson
- Department of Radiological Sciences, University of California Los Angeles, USA
| | - M Albert Thomas
- Department of Radiological Sciences, University of California Los Angeles, USA
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Simpson R, Devenyi GA, Jezzard P, Hennessy TJ, Near J. Advanced processing and simulation of
MRS
data using the
FID
appliance (
FID‐A
)—An open source,
MATLAB
‐based toolkit. Magn Reson Med 2015; 77:23-33. [DOI: 10.1002/mrm.26091] [Citation(s) in RCA: 164] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Revised: 11/24/2015] [Accepted: 11/24/2015] [Indexed: 02/04/2023]
Affiliation(s)
- Robin Simpson
- Department of Radiology, Medical PhysicsFreiburg UniversityFreiburg Germany
| | - Gabriel A. Devenyi
- Centre d'Imagerie CérébraleDouglas Mental Health University InstituteMontreal Canada
| | - Peter Jezzard
- FMRIB Centre, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxford UK
| | - T. Jay Hennessy
- Centre d'Imagerie CérébraleDouglas Mental Health University InstituteMontreal Canada
- Department of Biomedical EngineeringMcGill UniversityMontreal Canada
| | - Jamie Near
- Centre d'Imagerie CérébraleDouglas Mental Health University InstituteMontreal Canada
- Department of Biomedical EngineeringMcGill UniversityMontreal Canada
- Department of PsychiatryMcGill UniversityMontreal Canada
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Martel D, Tse Ve Koon K, Le Fur Y, Ratiney H. Localized 2D COSY sequences: Method and experimental evaluation for a whole metabolite quantification approach. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2015; 260:98-108. [PMID: 26432399 DOI: 10.1016/j.jmr.2015.09.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2014] [Revised: 09/03/2015] [Accepted: 09/04/2015] [Indexed: 05/08/2023]
Abstract
Two-dimensional spectroscopy offers the possibility to unambiguously distinguish metabolites by spreading out the multiplet structure of J-coupled spin systems into a second dimension. Quantification methods that perform parametric fitting of the 2D MRS signal have recently been proposed for resolved PRESS (JPRESS) but not explicitly for Localized Correlation Spectroscopy (LCOSY). Here, through a whole metabolite quantification approach, correlation spectroscopy quantification performances are studied. The ability to quantify metabolite relaxation constant times is studied for three localized 2D MRS sequences (LCOSY, LCTCOSY and the JPRESS) in vitro on preclinical MR systems. The issues encountered during implementation and quantification strategies are discussed with the help of the Fisher matrix formalism. The described parameterized models enable the computation of the lower bound for error variance--generally known as the Cramér Rao bounds (CRBs), a standard of precision--on the parameters estimated from these 2D MRS signal fittings. LCOSY has a theoretical net signal loss of two per unit of acquisition time compared to JPRESS. A rapid analysis could point that the relative CRBs of LCOSY compared to JPRESS (expressed as a percentage of the concentration values) should be doubled but we show that this is not necessarily true. Finally, the LCOSY quantification procedure has been applied on data acquired in vivo on a mouse brain.
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Affiliation(s)
- Dimitri Martel
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Claude Bernard Lyon 1, France
| | - K Tse Ve Koon
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Claude Bernard Lyon 1, France
| | - Yann Le Fur
- Aix-Marseille Université, CRMBM, CNRS UMR, 7339 Marseille, France
| | - Hélène Ratiney
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Claude Bernard Lyon 1, France.
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45
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MacMillan EL, Bolliger CS, Boesch C, Kreis R. Influence of muscle fiber orientation on water and metabolite relaxation times, magnetization transfer, and visibility in human skeletal muscle. Magn Reson Med 2015; 75:1764-70. [PMID: 25982125 DOI: 10.1002/mrm.25778] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2015] [Revised: 04/09/2015] [Accepted: 04/27/2015] [Indexed: 11/07/2022]
Abstract
PURPOSE To gain a deeper understanding of the influence of skeletal muscle fiber orientation on metabolite visibility, magnetization transfer from water, and water proton relaxation rates in (1)H MR spectra. METHODS Non-water-suppressed MR spectroscopy was performed in tibialis anterior muscle (TA) of 10 healthy adults, with the TA oriented either parallel or at the magic angle to the 3T field. Spectra were acquired with metabolite-cycled PRESS, and water inversion from 50 to 2510 ms before excitation. Water proton T2 relaxation was sampled with STEAM with echo times from 12 to 272 ms. RESULTS Apparent concentrations of total creatine (tCr), taurine, and trimethylammonium compounds were reduced by 29% to 67% when TA was parallel to B0. Both tCr peak areas were strongly correlated to the methylene peak splitting. Magnetization transfer rates from water to tCr CH3 were not significantly different between orientations. Water T1s were similar between orientations, but T2s were statistically significantly shorter by 1 ms in the parallel orientation (P = 0.002). CONCLUSION Muscle metabolite visibilities in MR spectroscopy and water T2 times depend substantially on muscle fiber orientation relative to B0 . In contrast, magnetization transfer rates appear to depend on muscle composition, rather than fiber orientation.
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Affiliation(s)
- Erin Leigh MacMillan
- Department of Clinical Research and Institute of Diagnostic, Interventional and Pediatric Radiology, University of Bern, Bern, Switzerland.,Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Christine Sandra Bolliger
- Department of Clinical Research and Institute of Diagnostic, Interventional and Pediatric Radiology, University of Bern, Bern, Switzerland
| | - Chris Boesch
- Department of Clinical Research and Institute of Diagnostic, Interventional and Pediatric Radiology, University of Bern, Bern, Switzerland
| | - Roland Kreis
- Department of Clinical Research and Institute of Diagnostic, Interventional and Pediatric Radiology, University of Bern, Bern, Switzerland
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46
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Automatic voxel positioning for MRS at 7 T. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2014; 28:259-70. [PMID: 25408107 DOI: 10.1007/s10334-014-0469-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2014] [Revised: 10/16/2014] [Accepted: 10/21/2014] [Indexed: 10/24/2022]
Abstract
OBJECT The purpose of this study was to test, for the first time, whether spectroscopy voxels could be positioned automatically with high accuracy and reproducibility in ultrahigh-field longitudinal magnetic resonance spectroscopy (MRS) studies. MATERIALS AND METHODS MRS voxels were automatically positioned in two cingulate subregions of 12 healthy subjects using a vendor-provided automatic voxel positioning (AutoAlign) technique, and were manually placed in the same regions of 10 healthy subjects by an experienced technician in three 7 T MRS scan sessions. Different coils were used for manual (24-channel coil) and automatic (32-channel coil) voxel placement, and the effects of signal-to-noise-ratio differences on the spectra were considered. RESULTS Over three scan sessions and two regions scanned for each subject, a mean voxel geometric overlap ratio of 0.91 for automatic positioning reflected accurate voxel alignment, while the geometric overlap ratio was only 0.70 for voxels placed manually. Comparable voxel positions among the three scan sessions (p > 0.05) indicated high reproducibility of automatic voxel alignment. In comparison, significant voxel displacement among scan sessions (p < 0.05) was found using manual voxel positioning. CONCLUSIONS In view of the highly accurate and reproducible voxel alignment with automatic voxel positioning, we propose the application of automatic rather than manual voxel positioning in future ultrahigh-field longitudinal MRS studies.
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47
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Snoussi K, Gillen JS, Horska A, Puts NAJ, Pradhan S, Edden RAE, Barker PB. Comparison of brain gray and white matter macromolecule resonances at 3 and 7 Tesla. Magn Reson Med 2014; 74:607-13. [PMID: 25252131 DOI: 10.1002/mrm.25468] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Revised: 08/06/2014] [Accepted: 08/29/2014] [Indexed: 12/26/2022]
Abstract
PURPOSE In proton MR spectra of the human brain, relatively broad macromolecule (MM) resonances underlie the narrower signals from metabolites. The purpose of this study was to quantify the MM profile in healthy human brain at 3T and 7T, both in gray matter (anterior cingulate cortex [ACC]) and white matter (centrum semiovale [CSO]). METHODS A water-suppressed, inversion-recovery pulse sequence was used to null metabolite signals and acquire MM spectra in 20 healthy volunteers using very similar methodology at both field strengths (n = 5 per region and field). The MM spectra were fitted with multiple Gaussian functions and quantified relative to the unsuppressed water signal from the same volume. RESULTS MM proton concentration values were in the range of 5-20 mmol/kg. No significant differences were found between the MM proton concentration measurements by region (P ≈ 0.8) nor by field strength (P ≈ 0.5). Linewidths of the well-resolved M1 peak were slightly more than double at 7T (43.0 ± 4.7 Hz in ACC, 45.6 ± 4.1 Hz in CSO) compared with 3T (19.8 ± 3.5 Hz in ACC, 20.0 ± 4.3 Hz in CSO). CONCLUSION The absence of differences in MM concentrations between white and gray matter implies that a single MM "baseline" may be adequate for spectral fitting of multiple brain regions when determining metabolite concentrations. Visibility of MM signals is similar at 3T and 7T.
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Affiliation(s)
- Karim Snoussi
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Joseph S Gillen
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Alena Horska
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Nicolaas A J Puts
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Subechhya Pradhan
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Peter B Barker
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, Maryland, USA
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48
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Homan P, Vermathen P, Van Swam C, Federspiel A, Boesch C, Strik W, Dierks T, Hubl D, Kreis R. Magnetic resonance spectroscopy investigations of functionally defined language areas in schizophrenia patients with and without auditory hallucinations. Neuroimage 2014; 94:23-32. [PMID: 24650602 DOI: 10.1016/j.neuroimage.2014.03.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2013] [Revised: 02/26/2014] [Accepted: 03/08/2014] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Cerebral dysfunction occurring in mental disorders can show metabolic disturbances which are limited to circumscribed brain areas. Auditory hallucinations have been shown to be related to defined cortical areas linked to specific language functions. Here, we investigated if the study of metabolic changes in auditory hallucinations requires a functional rather than an anatomical definition of their location and size to allow a reliable investigation by magnetic resonance spectroscopy (MRS). METHODS Schizophrenia patients with (AH; n=12) and without hallucinations (NH; n=8) and healthy controls (HC; n=11) underwent a verbal fluency task in functional MRI (fMRI) to functionally define Broca's and Wernicke's areas. Left and right Heschl's gyri were defined anatomically. RESULTS The mean distances in native space between the fMRI-defined regions and a corresponding anatomically defined area were 12.4±6.1 mm (range: 2.7-36.1 mm) for Broca's area and 16.8±6.2 mm (range: 4.5-26.4 mm) for Wernicke's area, respectively. Hence, the spatial variance was of similar extent as the size of the investigated regions. Splitting the investigations into a single voxel examination in the frontal brain and a spectroscopic imaging part for the more homogeneous field areas led to good spectral quality for almost all spectra. In Broca's area, there was a significant group effect (p=0.03) with lower levels of N-acetyl-aspartate (NAA) in NH compared to HC (p=0.02). There were positive associations of NAA levels in the left Heschl's gyrus with total (p=0.03) and negative (p=0.006) PANSS scores. In Broca's area, there was a negative association of myo-inositol levels with total PANSS scores (p=0.008). CONCLUSION This study supports the neurodegenerative hypothesis of schizophrenia only in a frontal region whereas the results obtained from temporal regions are in contrast to the majority of previous studies. Future research should test the hypothesis raised by this study that a functional definition of language regions is needed if neurochemical imbalances are expected to be restricted to functional foci.
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Affiliation(s)
- Philipp Homan
- Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Switzerland
| | - Peter Vermathen
- Unit for Magnetic Resonance Spectroscopy and Methodology, Department of Clinical Research and Institute of Diagnostic, Interventional and Pediatric Radiology, University of Bern, Switzerland
| | - Claudia Van Swam
- Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Switzerland
| | - Andrea Federspiel
- Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Switzerland
| | - Chris Boesch
- Unit for Magnetic Resonance Spectroscopy and Methodology, Department of Clinical Research and Institute of Diagnostic, Interventional and Pediatric Radiology, University of Bern, Switzerland
| | - Werner Strik
- University Hospital of Psychiatry, University of Bern, Switzerland
| | - Thomas Dierks
- Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Switzerland.
| | - Daniela Hubl
- Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Switzerland
| | - Roland Kreis
- Unit for Magnetic Resonance Spectroscopy and Methodology, Department of Clinical Research and Institute of Diagnostic, Interventional and Pediatric Radiology, University of Bern, Switzerland
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Brandejsky V, Boesch C, Kreis R. Proton diffusion tensor spectroscopy of metabolites in human muscle in vivo. Magn Reson Med 2014; 73:481-7. [PMID: 24554491 DOI: 10.1002/mrm.25139] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2013] [Revised: 11/27/2013] [Accepted: 01/02/2014] [Indexed: 01/08/2023]
Abstract
PURPOSE To study the apparent diffusivity and its directionality for metabolites of skeletal muscle in humans in vivo by (1) H magnetic resonance spectroscopy. METHODS The diffusion tensors were determined on a 3 Tesla MR system using optimized acquisition and processing methods including an adapted STEAM sequence with orientation-dependent diffusion weighting, pulse-triggering with individually adapted delays, eddy-current correction schemes, median filtering, and simultaneous prior-knowledge fitting of all related spectra. RESULTS The average apparent diffusivities, as well as the fractional anisotropies of taurine (ADCav=0.74 × 10(-3) s/mm(2) , FA=0.46), creatine (ADCav =0.41 × 10(-3) s/mm(2) , FA=0.33), trimethylammonium compounds (ADCav =0.48 × 10(-3) s/mm(2) , FA=0.34), carnosine (ADCav =0.46 × 10(-3) s/mm(2) , FA=0.47), and water (ADCav=1.5 × 10(-3) s/mm(2) , FA=0.36) were estimated. The diffusivities of most metabolites and water were significantly different from each other. Diffusion was found to be anisotropic and the diffusion tensors showed tensor correlation coefficients close to 1 and were hence found to be essentially coaligned. The magnitudes of apparent metabolite diffusivities were largely ordered according to molecular weight, with taurine as the smallest molecule diffusing fastest, both along and across the fiber direction. CONCLUSION Diffusivities, directional dependence of diffusion and fractional anisotropies of (1) H MRS-visible muscle metabolites were presented. It was shown that metabolites share diffusion directionality with water and have similar fractional anisotropies, hinting at similar diffusion barriers.
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Affiliation(s)
- Vaclav Brandejsky
- Unit for Magnetic Resonance Spectroscopy and Methodology, Institute of Diagnostic, Interventional and Pediatric Radiology and Department of Clinical Research, University of Bern, Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
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50
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Lecoultre V, Carrel G, Egli L, Binnert C, Boss A, MacMillan EL, Kreis R, Boesch C, Darimont C, Tappy L. Coffee consumption attenuates short-term fructose-induced liver insulin resistance in healthy men. Am J Clin Nutr 2014; 99:268-75. [PMID: 24257718 DOI: 10.3945/ajcn.113.069526] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Epidemiologic and experimental data have suggested that chlorogenic acid, which is a polyphenol contained in green coffee beans, prevents diet-induced hepatic steatosis and insulin resistance. OBJECTIVE We assessed whether the consumption of chlorogenic acid-rich coffee attenuates the effects of short-term fructose overfeeding, dietary conditions known to increase intrahepatocellular lipids (IHCLs), and blood triglyceride concentrations and to decrease hepatic insulin sensitivity in healthy humans. DESIGN Effects of 3 different coffees were assessed in 10 healthy volunteers in a randomized, controlled, crossover trial. IHCLs, hepatic glucose production (HGP) (by 6,6-d2 glucose dilution), and fasting lipid oxidation were measured after 14 d of consumption of caffeinated coffee high in chlorogenic acid (C-HCA), decaffeinated coffee high in chlorogenic acid, or decaffeinated coffee with regular amounts of chlorogenic acid (D-RCA); during the last 6 d of the study, the weight-maintenance diet of subjects was supplemented with 4 g fructose · kg(-1) · d(-1) (total energy intake ± SD: 143 ± 1% of weight-maintenance requirements). All participants were also studied without coffee supplementation, either with 4 g fructose · kg(-1) · d(-1) (high fructose only) or without high fructose (control). RESULTS Compared with the control diet, the high-fructose diet significantly increased IHCLs by 102 ± 36% and HGP by 16 ± 3% and decreased fasting lipid oxidation by 100 ± 29% (all P < 0.05). All 3 coffees significantly decreased HGP. Fasting lipid oxidation increased with C-HCA and D-RCA (P < 0.05). None of the 3 coffees significantly altered IHCLs. CONCLUSIONS Coffee consumption attenuates hepatic insulin resistance but not the increase of IHCLs induced by fructose overfeeding. This effect does not appear to be mediated by differences in the caffeine or chlorogenic acid content. This trial was registered at clinicaltrials.gov as NCT00827450.
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Affiliation(s)
- Virgile Lecoultre
- Department of Physiology, University of Lausanne, Lausanne, Switzerland (VL, GC, LE, C Binnert, and LT); the Service of Internal Medicine (GC) and the Service of Endocrinology, Diabetes and Metabolism (LT); Lausanne University Hospital, Lausanne, Switzerland; the Department of Clinical Research and Institute of Diagnostic, Interventional, and Pediatric Radiology, University Bern, Bern, Switzerland (AB, ELM, RK, and C Boesch); and Nutrition & Health Research, Nestlé Research Center, Nestec SA, Lausanne, Switzerland (C Binnert and CD)
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