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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. Magn Reson Med 2024; 92:2222-2236. [PMID: 38988088 PMCID: PMC11341254 DOI: 10.1002/mrm.30209] [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: 04/01/2024] [Revised: 06/09/2024] [Accepted: 06/19/2024] [Indexed: 07/12/2024]
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 2D 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 SD 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 FPC 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, for example, 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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Davies-Jenkins CW, Zöllner HJ, Simicic D, Hui SCN, Song Y, Hupfeld KE, Prisciandaro JJ, Edden RA, Oeltzschner G. GABA-edited MEGA-PRESS at 3 T: Does a measured macromolecule background improve linear combination modeling? Magn Reson Med 2024; 92:1348-1362. [PMID: 38818623 PMCID: PMC11262975 DOI: 10.1002/mrm.30158] [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: 09/06/2023] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 06/01/2024]
Abstract
PURPOSE The J-difference edited γ-aminobutyric acid (GABA) signal is contaminated by other co-edited signals-the largest of which originates from co-edited macromolecules (MMs)-and is consequently often reported as "GABA+." MM signals are broader and less well-characterized than the metabolites, and are commonly approximated using a Gaussian model parameterization. Experimentally measured MM signals are a consensus-recommended alternative to parameterized modeling; however, they are relatively under-studied in the context of edited MRS. METHODS To address this limitation in the literature, we have acquired GABA-edited MEGA-PRESS data with pre-inversion to null metabolite signals in 13 healthy controls. An experimental MM basis function was derived from the mean across subjects. We further derived a new parameterization of the MM signals from the experimental data, using multiple Gaussians to accurately represent their observed asymmetry. The previous single-Gaussian parameterization, mean experimental MM spectrum and new multi-Gaussian parameterization were compared in a three-way analysis of a public MEGA-PRESS dataset of 61 healthy participants. RESULTS Both the experimental MMs and the multi-Gaussian parameterization exhibited reduced fit residuals compared to the single-Gaussian approach (p = 0.034 and p = 0.031, respectively), suggesting they better represent the underlying data than the single-Gaussian parameterization. Furthermore, both experimentally derived models estimated larger MM fractional contribution to the GABA+ signal for the experimental MMs (58%) and multi-Gaussian parameterization (58%), compared to the single-Gaussian approach (50%). CONCLUSIONS Our results indicate that single-Gaussian parameterization of edited MM signals is insufficient and that both experimentally derived GABA+ spectra and their parameterized replicas improve the modeling of GABA+ spectra.
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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, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Helge J. Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Dunja Simicic
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Steve C. N. Hui
- Developing Brain Institute, Children’s National Hospital, Washington, DC, USA
- Department of Radiology, The George Washington School of Medicine and Health Sciences, Washington D.C., USA
- Department of Pediatrics, The George Washington School of Medicine and Health Sciences, Washington D.C., USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Kathleen E. Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - James J. Prisciandaro
- Department of Psychiatry and Behavioral Sciences, Addiction Sciences Division, Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA
| | - Richard A.E. Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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Pasmiño D, Slotboom J, Schweisthal B, Guevara P, Valenzuela W, Pino EJ. Comparison of baseline correction algorithms for in vivo 1H-MRS. NMR IN BIOMEDICINE 2024:e5203. [PMID: 38953695 DOI: 10.1002/nbm.5203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 05/08/2024] [Accepted: 05/29/2024] [Indexed: 07/04/2024]
Abstract
Proton MRS is used clinically to collect localized, quantitative metabolic data from living tissues. However, the presence of baselines in the spectra complicates accurate MRS data quantification. The occurrence of baselines is not specific to short-echo-time MRS data. In short-echo-time MRS, the baseline consists typically of a dominating macromolecular (MM) part, and can, depending on B0 shimming, poor voxel placement, and/or localization sequences, also contain broad water and lipid resonance components, indicated by broad components (BCs). In long-echo-time MRS, the MM part is usually much smaller, but BCs may still be present. The sum of MM and BCs is denoted by the baseline. Many algorithms have been proposed over the years to tackle these artefacts. A first approach is to identify the baseline itself in a preprocessing step, and a second approach is to model the baseline in the quantification of the MRS data themselves. This paper gives an overview of baseline handling algorithms and also proposes a new algorithm for baseline correction. A subset of suitable baseline removal algorithms were tested on in vivo MRSI data (semi-LASER at TE = 40 ms) and compared with the new algorithm. The baselines in all datasets were removed using the different methods and subsequently fitted using spectrIm-QMRS with a TDFDFit fitting model that contained only a metabolite basis set and lacked a baseline model. The same spectra were also fitted using a spectrIm-QMRS model that explicitly models the metabolites and the baseline of the spectrum. The quantification results of the latter quantification were regarded as ground truth. The fit quality number (FQN) was used to assess baseline removal effectiveness, and correlations between metabolite peak areas and ground truth models were also examined. The results show a competitive performance of our new proposed algorithm, underscoring its automatic approach and efficiency. Nevertheless, none of the tested baseline correction methods achieved FQNs as good as the ground truth model. All separately applied baseline correction methods introduce a bias in the observed metabolite peak areas. We conclude that all baseline correction methods tested, when applied as a separate preprocessing step, yield poorer FQNs and biased quantification results. While they may enhance visual display, they are not advisable for use before spectral fitting.
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Affiliation(s)
- Diego Pasmiño
- Electrical Engineering Department, Universidad de Concepcion, Concepcion, Chile
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging (SCAN), Neuroradiology, University Hospital Inselspital, Bern, Switzerland
| | - Brigitte Schweisthal
- Support Center for Advanced Neuroimaging (SCAN), Neuroradiology, University Hospital Inselspital, Bern, Switzerland
- Politehnica University Timișoara, Timișoara, Romania
| | - Pamela Guevara
- Electrical Engineering Department, Universidad de Concepcion, Concepcion, Chile
| | - Waldo Valenzuela
- Support Center for Advanced Neuroimaging (SCAN), Neuroradiology, University Hospital Inselspital, Bern, Switzerland
| | - Esteban J Pino
- Electrical Engineering Department, Universidad de Concepcion, Concepcion, Chile
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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.) 2024; 37:477-489. [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] [MESH Headings] [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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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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Dell'Orco A, Riemann LT, Ellison SLR, Aydin S, Göschel L, Tietze A, Scheel M, Fillmer A. Macromolecule modelling for improved metabolite quantification using short echo time brain 1 H MRS at 3 T and 7 T: The PRaMM Model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.16.567383. [PMID: 38014000 PMCID: PMC10680753 DOI: 10.1101/2023.11.16.567383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Purpose To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain 1 H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Methods Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared to those other methods was investigated. Results The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. While the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p≤0.0001). Minimally detectable changes are in the range 0.5 - 1.9 mM and percent coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out. Conclusion Here, the PRaMM model, a method for an improved quantification of metabolites was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.
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An L, Shen J. In vivo magnetic resonance spectroscopy by transverse relaxation encoding with narrowband decoupling. Sci Rep 2023; 13:12211. [PMID: 37500714 PMCID: PMC10374641 DOI: 10.1038/s41598-023-39375-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023] Open
Abstract
Cell pathology in neuropsychiatric disorders has mainly been accessible by analyzing postmortem tissue samples. Although molecular transverse relaxation informs local cellular microenvironment via molecule-environment interactions, precise determination of the transverse relaxation times of molecules with scalar couplings (J), such as glutamate and glutamine, has been difficult using in vivo magnetic resonance spectroscopy (MRS) technologies, whose approach to measuring transverse relaxation has not changed for decades. We introduce an in vivo MRS technique that utilizes frequency-selective editing pulses to achieve homonuclear decoupled chemical shift encoding in each column of the acquired two-dimensional dataset, freeing up the entire row dimension for transverse relaxation encoding with J-refocusing. This results in increased spectral resolution, minimized background signals, and markedly broadened dynamic range for transverse relaxation encoding. The in vivo within-subject coefficients of variation for the transverse relaxation times of glutamate and glutamine, measured using the proposed method in the human brain at 7 T, were found to be approximately 4%. Since glutamate predominantly resides in glutamatergic neurons and glutamine in glia in the brain, this noninvasive technique provides a way to probe cellular pathophysiology in neuropsychiatric disorders for characterizing disease progression and monitoring treatment response in a cell type-specific manner in vivo.
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Affiliation(s)
- Li An
- Molecular Imaging Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
- Molecular Imaging Branch, National Institute of Mental Health, National Institutes of Health, Building 10, Room 3D46, 10 Center Drive, MSC 1216, Bethesda, MD, 20892-1216, USA.
| | - Jun Shen
- Molecular Imaging Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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Okada T, Kuribayashi H, Urushibata Y, Fujimoto K, Akasaka T, Seethamraju RT, Ahn S, Isa T. GABA, glutamate and excitatory-inhibitory ratios measured using short-TE STEAM MRS at 7-Tesla: Effects of macromolecule basis sets and baseline parameters. Heliyon 2023; 9:e18357. [PMID: 37539101 PMCID: PMC10393741 DOI: 10.1016/j.heliyon.2023.e18357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 07/10/2023] [Accepted: 07/14/2023] [Indexed: 08/05/2023] Open
Abstract
Rationale and objectives Macromolecules (MMs) affect the precision and accuracy of neurochemical quantification in magnetic resonance spectroscopy. A measured MM basis is increasingly used in LCModel analysis combined with a spline baseline, whose stiffness is controlled by a parameter named DKNTMN. The effects of measured MM basis and DKNTMN were investigated. Materials and methods Twenty-six healthy subjects were prospectively enrolled and scanned twice using a short echo-time Stimulated Echo Acquisition Mode (STEAM) at 7-T. Using LCModel, analyses were conducted using the simulated MM basis (MMsim) with DKNTMN 0.15 and an MM basis measured inhouse (MMmeas) with DKNTMN of 0.15, 0.30, 0.60 and 1.00. Cramér-Rao lower bound (CRLB) and the concentrations of gamma-aminobutyric acid (GABA), glutamate and excitatory-inhibitory ratio (EIR), in addition to MMs were statistically analyzed. Measurement stability was evaluated using coefficient of variation (CV). Results CRLBs of GABA were significantly lower when using MMsim than MMmeas; those of glutamate were 2-3. GABA concentrations were significantly higher in the analysis using MMsim than MMmeas where concentrations were significantly higher with DKNTMN of 0.15 or 0.30 than 0.60 or 1.00. Difference in glutamate concentration was not significant. EIRs showed the same difference as in GABA depending on the DKNTMN values. CVs between test-retest scans were relatively stable for glutamate but became larger as DKNTMN increased for GABA and EIR. Conclusion Neurochemical quantification depends on the parameters of the basis sets used for fitting. Analysis using MMmeas with DKNTMN of 0.30 conformed best to previous studies and is recommended.
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Affiliation(s)
| | | | | | - Koji Fujimoto
- Human Brain Research Center, Tokyo, Japan
- Department of Real World Data Research and Development, Graduate School of Medicine, Kyoto University, Japan
| | | | | | - Sinyeob Ahn
- Siemens Medical Solutions, Berkeley, California, USA
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10
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Hong S, Shen J. Neurochemical correlations in short echo time proton magnetic resonance spectroscopy. NMR IN BIOMEDICINE 2023; 36:e4910. [PMID: 36681860 DOI: 10.1002/nbm.4910] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 06/15/2023]
Abstract
Neurochemical concentrations determined by magnetic resonance spectroscopy (MRS) have been treated as statistically independent measurements in various clinical MRS studies. However, spectral overlap, independent of any biological effects, could lead to significant correlations between neurochemical concentrations extracted from spectral fitting of MRS data, confounding determination of correlations of biological origin. Short echo time (TE) proton MRS spectra are very crowded because of the comparatively narrow chemical shift dispersion of proton nuclear spins. In this study, the complex neurochemical correlations of spectral origin in short-TE MRS spectra were quantified. The effects of macromolecules and the background spectral baseline on metabolite-metabolite correlations were also analyzed. Our results demonstrate the importance of factoring in spectral correlations when correlating overlapping metabolite signals in short-TE spectra with clinical parameters.
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Affiliation(s)
- Sungtak Hong
- Section on Magnetic Resonance Spectroscopy, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA
| | - Jun Shen
- Section on Magnetic Resonance Spectroscopy, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA
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11
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Salan T, Willen EJ, Cuadra A, Sheriff S, Maudsley AA, Govind V. Whole-brain MR spectroscopic imaging reveals regional metabolite abnormalities in perinatally HIV infected young adults. Front Neurosci 2023; 17:1134867. [PMID: 36937663 PMCID: PMC10017464 DOI: 10.3389/fnins.2023.1134867] [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: 12/31/2022] [Accepted: 02/13/2023] [Indexed: 03/06/2023] Open
Abstract
Perinatally acquired HIV (PHIV) has been associated with brain structural and functional deficiencies, and with poorer cognitive performance despite the advent of antiretroviral therapy (ART). However, investigation of brain metabolite levels in PHIV measured by proton magnetic resonance spectroscopy (MRS) methods, is still limited with often inconclusive or contradictory findings. In general, these MRS-based methods have used a single voxel approach that can only evaluate metabolite concentrations in a few select brain anatomical regions. Additionally, most of the published data have been on children perinatally infected with HIV with only a few studies examining adult populations, though not exclusively. Therefore, this prospective and cross-sectional study aims to evaluate metabolite differences at the whole-brain level, using a unique whole-brain proton magnetic resonance spectroscopy imaging (MRSI) method, in a group of PHIV infected young adults (N = 28) compared to age and gender matched control sample (N = 28), and to find associations with HIV clinical factors and neurocognitive scores. MRSI data were acquired on a 3T scanner with a TE of 70 ms. Brain metabolites levels of total N-acetylaspartate (tNAA), total choline (tCho) and total creatine (tCre), as well as ratios of tNAA/tCre, tCho/tCre, and tNAA/tCho, were obtained from the whole brain level and evaluated at the level of gray matter (GM) and white matter (WM) tissue types and anatomical regions of interest (ROI). Our results indicate extensive metabolic abnormalities throughout the brains of PHIV infected subjects with significantly elevated levels of tCre and tCho, notably in GM regions. Decreases in tNAA and ratios of tNAA/tCre and tNAA/tCho were also found mostly in WM regions. These metabolic alterations indicate increased glial activation, inflammation, neuronal dysfunction, and energy metabolism in PHIV infected individuals, which correlated with a reduction in CD4 cell count, and lower cognitive scores. Our findings suggest that significant brain metabolite alterations and associated neurological complications persist in the brains of those with PHIV on long-term ART, and advocates the need for continued monitoring of their brain health.
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Affiliation(s)
- Teddy Salan
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Elizabeth J. Willen
- Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, MO, United States
| | - Anai Cuadra
- Department of Pediatrics, Mailman Center for Child Development, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Sulaiman Sheriff
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Andrew A. Maudsley
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Varan Govind
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, United States
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12
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Zöllner HJ, Davies-Jenkins CW, Murali-Manohar S, Gong T, Hui SCN, Song Y, Chen W, Wang G, Edden RAE, Oeltzschner G. Feasibility and implications of using subject-specific macromolecular spectra to model short echo time magnetic resonance spectroscopy data. NMR IN BIOMEDICINE 2023; 36:e4854. [PMID: 36271899 PMCID: PMC9930668 DOI: 10.1002/nbm.4854] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/14/2022] [Accepted: 10/15/2022] [Indexed: 05/27/2023]
Abstract
Expert consensus recommends linear-combination modeling (LCM) of 1 H MR spectra with sequence-specific simulated metabolite basis function and experimentally derived macromolecular (MM) basis functions. Measured MM basis functions are usually derived from metabolite-nulled spectra averaged across a small cohort. The use of subject-specific instead of cohort-averaged measured MM basis functions has not been studied widely. Furthermore, measured MM basis functions are not widely available to non-expert users, who commonly rely on parameterized MM signals internally simulated by LCM software. To investigate the impact of the choice of MM modeling, this study, therefore, compares metabolite level estimates between different MM modeling strategies (cohort-mean measured; subject-specific measured; parameterized) in a lifespan cohort and characterizes its impact on metabolite-age associations. 100 conventional (TE = 30 ms) and metabolite-nulled (TI = 650 ms) PRESS datasets, acquired from the medial parietal lobe in a lifespan cohort (20-70 years of age), were analyzed in Osprey. Short-TE spectra were modeled in Osprey using six different strategies to consider the MM baseline. Fully tissue- and relaxation-corrected metabolite levels were compared between MM strategies. Model performance was evaluated by model residuals, the Akaike information criterion (AIC), and the impact on metabolite-age associations. The choice of MM strategy had a significant impact on the mean metabolite level estimates and no major impact on variance. Correlation analysis revealed moderate-to-strong agreement between different MM strategies (r > 0.6). The lowest relative model residuals and AIC values were found for the cohort-mean measured MM. Metabolite-age associations were consistently found for two major singlet signals (total creatine (tCr])and total choline (tCho)) for all MM strategies; however, findings for metabolites that are less distinguishable from the background signals associations depended on the MM strategy. A variance partition analysis indicated that up to 44% of the total variance was related to the choice of MM strategy. Additionally, the variance partition analysis reproduced the metabolite-age association for tCr and tCho found in the simpler correlation analysis. In summary, the inclusion of a single high signal-to-noise ratio MM basis function (cohort-mean) in the short-TE LCM leads to more lower model residuals and AIC values compared with MM strategies with more degrees of freedom (Gaussian parametrization) or subject-specific MM information. Integration of multiple LCM analyses into a single statistical model potentially allows to identify the robustness in the detection of underlying effects (e.g., metabolite vs. age), reduces algorithm-based bias, and estimates algorithm-related variance.
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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 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
| | - Saipavitra Murali-Manohar
- 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
| | - Tao Gong
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021,China
- Departments of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Jinan, Shandong, 250021,China
| | - Steve C. N. Hui
- 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
| | - Yulu Song
- 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
| | | | - Guangbin Wang
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021,China
- Departments of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Jinan, Shandong, 250021,China
| | - 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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13
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Ziegs T, Dorst J, Ruhm L, Avdievitch N, Henning A. Measurement of glucose metabolism in the occipital lobe and frontal cortex after oral administration of [1-13C]glucose at 9.4 T. J Cereb Blood Flow Metab 2022; 42:1890-1904. [PMID: 35632989 PMCID: PMC9536126 DOI: 10.1177/0271678x221104540] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 04/29/2022] [Accepted: 05/05/2022] [Indexed: 12/02/2022]
Abstract
For the first time, labeling effects after oral intake of [1-13C]glucose are observed in the human brain with pure 1H detection at 9.4 T. Spectral time series were acquired using a short-TE 1H MRS MC-semiLASER (Metabolite Cycling semi Localization by Adiabatic SElective Refocusing) sequence in two voxels of 5.4 mL in the frontal cortex and the occipital lobe. High-quality time-courses of [4-13C]glutamate, [4-13C]glutamine, [3-13C]glutamate + glutamine, [2-13C] glutamate+glutamine and [3-13C]aspartate for individual volunteers and additionally, group-averaged time-courses of labeled and non-labeled brain glucose could be obtained. Using a one-compartment model, mean metabolic rates were calculated for each voxel position: The mean rate of the TCA-cycle (Vtca) value was determined to be 1.36 and 0.93 μmol min-1 g-1, the mean rate of glutamine synthesis (Vgln) was calculated to be 0.23 and 0.45 μmol min-1 g-1, the mean exchange rate between cytosolic amino acids and mitochondrial Krebs cycle intermediates (Vx) rate was found to be 0.57 and 1.21 μmol min-1 g-1 for the occipital lobe and the frontal cortex, respectively. These values were in agreement with previously reported data. Altogether, it can be shown that this most simple technique combining oral administration of [1-13C]Glc with pure 1H MRS acquisition is suitable to measure metabolic rates.
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Affiliation(s)
- Theresia Ziegs
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Johanna Dorst
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Loreen Ruhm
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Nikolai Avdievitch
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Anke Henning
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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14
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Ziegs T, Wright AM, Henning A. Test-retest reproducibility of human brain multi-slice 1 H FID-MRSI data at 9.4T after optimization of lipid regularization, macromolecular model, and spline baseline stiffness. Magn Reson Med 2022; 89:11-28. [PMID: 36128885 DOI: 10.1002/mrm.29423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/28/2022] [Accepted: 08/02/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE This study analyzes the effects of retrospective lipid suppression, a simulated macromolecular prior knowledge and different spline baseline stiffness values on 9.4T multi-slice proton FID-MRSI data spanning the whole cerebrum of human brain and the reproducibility of respective metabolite ratio to total creatine (/tCr) maps for 10 brain metabolites. METHODS Measurements were performed twice on 5 volunteers using a short TR and TE FID MRSI 2D sequence at 9.4T. The effects of retrospective lipid L2-regularization, macromolecular spectrum and different LCModel baseline flexibilities on SNR, FWHM, fitting residual, Cramér-Rao lower bound, and metabolite ratio maps were investigated. Intra-subject, inter-session coefficient of variation and the test-retest reproducibility of the mean metabolite ratios (/tCr) of each slice was calculated. RESULTS Transversal, sagittal, and coronal slices of many metabolite ratio maps correspond to the anatomically expected concentration relations in gray and white matter for the majority of the cerebrum when using a flexible baseline in LCModel fit. Results from the second measurements of the same subjects show that slice positioning and data quality correlate significantly to the first measurement. L2-regularization provided effective suppression of lipid-artifacts, but should be avoided if no artifacts are detected. CONCLUSION Reproducible concentration ratio maps (/tCr) for 4 metabolites (total choline, N-acetylaspartate, glutamate, and myoinositol) spanning the majority of the cerebrum and 6 metabolites (N-acetylaspartylglutamate, γ-aminobutyric acid, glutathione, taurine, glutamine, and aspartate) covering 32 mm in the upper part of the brain were acquired at 9.4T using multi-slice FID MRSI with retrospective lipid suppression, a macromolecular spectrum and a flexible LCModel baseline.
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Affiliation(s)
- Theresia Ziegs
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Andrew Martin Wright
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Anke Henning
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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15
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Hong S, An L, Shen J. Monte Carlo study of metabolite correlations originating from spectral overlap. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 341:107257. [PMID: 35752065 PMCID: PMC9339476 DOI: 10.1016/j.jmr.2022.107257] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 06/02/2022] [Accepted: 06/10/2022] [Indexed: 05/28/2023]
Abstract
Monte Carlo simulations and a mathematical model of spectral fitting were used to study the correlations between metabolites with overlapping resonances. The dependence of the polarity and the magnitude of cross-correlation coefficients between overlapping metabolites on the spectral patterns of MRS signals was investigated. The results demonstrate the importance of quantifying metabolite correlations originating from spectral overlap as they may confound determination of correlations of biological origin. The findings also indicate that it is possible to minimize unwanted metabolite correlations by altering spectral patterns in the presence of significant spectral overlap.
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Affiliation(s)
- Sungtak Hong
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Li An
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Jun Shen
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
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16
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Kumaragamage C, Coppoli A, Brown PB, McIntyre S, Nixon TW, De Feyter HM, Mason GF, de Graaf RA. Short symmetric and highly selective asymmetric first and second order gradient modulated offset independent adiabaticity (GOIA) pulses for applications in clinical MRS and MRSI. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 341:107247. [PMID: 35691241 PMCID: PMC9933141 DOI: 10.1016/j.jmr.2022.107247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 05/04/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
Gradient modulated RF pulses, especially gradient offset independent adiabaticity (GOIA) pulses, are increasingly gaining attention for high field clinical magnetic resonance spectroscopy and spectroscopic imaging (MRS/MRSI) due to the lower peak B1 amplitude and associated power demands achievable relative to its non-modulated adiabatic full passage counterparts. In this work we describe the development of two GOIA RF pulses: 1) A power efficient, 3.0 ms wideband uniform rate with smooth truncation (WURST) modulated RF pulse with 15 kHz bandwidth compatible with a clinically feasible peak B1 amplitude of 0.87 kHz (or 20 µT), and 2) A highly selective asymmetric 6.66 ms RF pulse with 20 kHz bandwidth designed to achieve a single-sided, fractional transition width of only 1.7%. Effects of potential asynchrony between RF and gradient-modulated (GM) waveforms for 3 ms GOIA-WURST RF pulses was evaluated by simulation and experimentally. Results demonstrate that a 20+ µs asynchrony between RF and GM functions substantially degrades inversion performance when using large RF offsets to achieve translation. A projection-based method is presented that allows a quick calibration of RF and GM asynchrony on pre-clinical/clinical MR systems. The asymmetric GOIA pulse was implemented within a multi-pulse OVS sequence to achieve power efficient, highly-selective, and B1 and T1-independent signal suppression for extracranial lipid suppression. The developed GOIA pulses were utilized with linear gradient modulation (X, Y, Z gradient fields), and with second-order-field modulations (Z2, X2Y2 gradient fields) to provide elliptically-shaped regions-of-interest for MRS and MRSI acquisitions. Both described GOIA-RF pulses have substantial clinical value; specifically, the 3.0 ms GOIA-WURST pulse is beneficial to realize short TE sLASER localized proton MRS/MRSI sequences, and the asymmetric GOIA RF pulse has applications in highly selective outer volume signal suppression to allow interrogation of tissue proximal to extracranial lipids with full-intensity.
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Affiliation(s)
- Chathura Kumaragamage
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA.
| | - Anastasia Coppoli
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA
| | - Peter B Brown
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA
| | - Scott McIntyre
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA
| | - Terence W Nixon
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA
| | - Henk M De Feyter
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA
| | - Graeme F Mason
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Biomedical Engineering, Yale University School of Medicine, New Haven, CT, USA
| | - Robin A de Graaf
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA; Department of Biomedical Engineering, Yale University School of Medicine, New Haven, CT, USA
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17
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Dorst J, Borbath T, Landheer K, Avdievich N, Henning A. Simultaneous detection of metabolite concentration changes, water BOLD signal and pH changes during visual stimulation in the human brain at 9.4T. J Cereb Blood Flow Metab 2022; 42:1104-1119. [PMID: 35060409 PMCID: PMC9121534 DOI: 10.1177/0271678x221075892] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 12/14/2021] [Accepted: 12/22/2021] [Indexed: 12/15/2022]
Abstract
This study presents a method to directly link metabolite concentration changes and BOLD response in the human brain during visual stimulation by measuring the water and metabolite signals simultaneously. Therefore, the metabolite-cycling (MC) non-water suppressed semiLASER localization technique was optimized for functional 1H MRS in the human brain at 9.4 T. Data of 13 volunteers were acquired during a 26:40 min visual stimulation block-design paradigm. Activation-induced BOLD signal was observed in the MC water signal as well as in the NAA-CH3 and tCr-CH3 singlets. During stimulation, glutamate concentration increased 2.3 ± 2.0% to a new steady-state, while a continuous increase over the whole stimulation period could be observed in lactate with a mean increase of 35.6 ± 23.1%. These increases of Lac and Glu during brain activation confirm previous findings reported in literature. A positive correlation of the MC water BOLD signal with glutamate and lactate concentration changes was found. In addition, a pH decrease calculated from a change in the ratio of PCr to Cr was observed during brain activation, particularly at the onset of the stimulation.
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Affiliation(s)
- Johanna Dorst
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, University of Tübingen, Tübingen, Germany
| | - Tamas Borbath
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Faculty of Science, University of Tübingen, University of Tübingen, Tübingen, Germany
| | | | - Nikolai Avdievich
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Anke Henning
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA
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18
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Roussel T, Le Fur Y, Guye M, Viout P, Ranjeva JP, Callot V. Respiratory-triggered quantitative MR spectroscopy of the human cervical spinal cord at 7 T. Magn Reson Med 2022; 87:2600-2612. [PMID: 35181915 DOI: 10.1002/mrm.29182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 01/07/2022] [Accepted: 01/12/2022] [Indexed: 02/06/2023]
Abstract
PURPOSE Ultra-high field 1 H MR spectroscopy (MRS) is of great interest to help characterizing human spinal cord pathologies. However, very few studies have been reported so far in this small size structure at these fields due to challenging experimental difficulties caused by static and radiofrequency field heterogeneities, as well as physiological motion. In this work, in line with the recent developments proposed to strengthen spinal cord MRS feasibility at 7 T, a respiratory-triggered acquisition approach was optimized to compensate for dynamic B 0 field heterogeneities and to provide robust cervical spinal cord MRS data. METHODS A semi-LASER sequence was purposely used, and a dedicated raw data processing algorithm was developed to enhance MR spectral quality by discarding corrupted scans. To legitimate the choices done during the optimization stage, additional tests were carried out to determine the impact of breathing, voluntary motion, body mass index, and fitting algorithm. An in-house quantification tool was concomitantly designed for accurate estimation of the metabolite concentration ratios for choline, N-acetyl-aspartate (NAA), myo-inositol and glutathione. The method was tested on a cohort of 14 healthy volunteers. RESULTS Average water linewidth and NAA signal-to-noise ratio reached 0.04 ppm and 11.01, respectively. The group-average metabolic ratios were in good agreement with previous studies and showed intersession reproducibility variations below 30%. CONCLUSION The developed approach allows a rise of the acquired MRS signal quality and of the quantification robustness as compared to previous studies hence offering strengthened possibilities to probe the metabolism of degenerative and traumatic spinal cord pathologies.
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Affiliation(s)
- Tangi Roussel
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Yann Le Fur
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Patrick Viout
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Jean-Philippe Ranjeva
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Virginie Callot
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
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19
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Finkelman T, Furman-Haran E, Paz R, Tal A. Quantifying the excitatory-inhibitory balance: A comparison of SemiLASER and MEGA-SemiLASER for simultaneously measuring GABA and glutamate at 7T. Neuroimage 2021; 247:118810. [PMID: 34906716 DOI: 10.1016/j.neuroimage.2021.118810] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 12/08/2021] [Accepted: 12/10/2021] [Indexed: 10/19/2022] Open
Abstract
The importance of the excitatory-inhibitory (E/I) balance in a wide range of cognitive and behavioral processes has prompted a commensurate interest in methods for reliably quantifying it. Proton Magnetic Resonance Spectroscopy (1H-MRS) remains the only method capable of safely and non-invasively measuring the concentrations of the brain's major excitatory (glutamate) and inhibitory (γ-aminobutyric-acid, GABA) neurotransmitters in-vivo. MRS relies on spectral Mescher-Garwood (MEGA) editing techniques at 3T to distinguish GABA from its overlapping resonances. However, with the increased spectral resolution at ultrahigh field strengths of 7T and above, non-edited spectroscopic techniques become potential viable alternatives to MEGA based approaches, and also address some of their shortcomings, such as signal loss, sensitivity to transmitter inhomogeneities and temporal resolution. We present a comprehensive comparison of both edited and non-edited strategies at 7T for simultaneously quantifying glutamate and GABA from the dorsal anterior cingulate cortex (dACC), and evaluate their reproducibility and relative bias. The combined root-mean-square test-retest reproducibility of Glu and GABA (CVE/I) was as low as 13.3% for unedited MRS at TE=80 ms using SemiLASER localization, while edited MRS at TE=80 ms yielded CVE/I=20% and 21% for asymmetric and symmetric MEGA editing, respectively. An unedited SemiLASER acquisition using a shorter echo time of TE=42 ms yielded CVE/I as low as 24.9%. Our results show that non-edited sequences at an echo time of 80 ms provide better reproducibility than either edited sequences at the same TE, or non-edited sequences at a shorter TE of 42 ms. This is supported by numerical simulations and is driven in part by a pseudo-singlet appearance of the GABA multiplets at TE=80 ms, and the excellent spectral resolution at 7T. Our results uphold a transition to non-edited MRS for monitoring the E/I balance at ultrahigh fields, and stress the importance of using a properly-optimized echo time.
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Affiliation(s)
- Tal Finkelman
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel; Department of Chemical and Biological Physics, Weizmann Institute of Science, 234 Herzel St., Rehovot 7610001, Israel
| | - Edna Furman-Haran
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Rony Paz
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, 234 Herzel St., Rehovot 7610001, Israel.
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20
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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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21
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Gajdošík M, Landheer K, Swanberg KM, Adlparvar F, Madelin G, Bogner W, Juchem C, Kirov II. Hippocampal single-voxel MR spectroscopy with a long echo time at 3 T using semi-LASER sequence. NMR IN BIOMEDICINE 2021; 34:e4538. [PMID: 33956374 PMCID: PMC10874619 DOI: 10.1002/nbm.4538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 04/01/2021] [Accepted: 04/19/2021] [Indexed: 06/12/2023]
Abstract
The hippocampus is one of the most challenging brain regions for proton MR spectroscopy (MRS) applications. Moreover, quantification of J-coupled species such as myo-inositol (m-Ins) and glutamate + glutamine (Glx) is affected by the presence of macromolecular background. While long echo time (TE) MRS eliminates the macromolecules, it also decreases the m-Ins and Glx signal and, as a result, these metabolites are studied mainly with short TE. Here, we investigate the feasibility of reproducibly measuring their concentrations at a long TE of 120 ms, using a semi-adiabatic localization by adiabatic selective refocusing (sLASER) sequence, as this sequence was recently recommended as a standard for clinical MRS. Gradient offset-independent adiabatic refocusing pulses were implemented, and an optimal long TE for the detection of m-Ins and Glx was determined using the T2 relaxation times of macromolecules. Metabolite concentrations and their coefficients of variation (CVs) were obtained for a 3.4-mL voxel centered on the left hippocampus on 3-T MR systems at two different sites with three healthy subjects (aged 32.5 ± 10.2 years [mean ± standard deviation]) per site, with each subject scanned over two sessions, and with each session comprising three scans. Concentrations of m-Ins, choline, creatine, Glx and N-acetyl-aspartate were 5.4 ± 1.5, 1.7 ± 0.2, 5.8 ± 0.3, 11.6 ± 1.2 and 5.9 ± 0.4 mM (mean ± standard deviation), respectively. Their respective mean within-session CVs were 14.5% ± 5.9%, 6.5% ± 5.3%, 6.0% ± 3.4%, 10.6% ± 6.2% and 3.5% ± 1.4%, and their mean within-subject CVs were 17.8% ± 18.2%, 7.5% ± 6.3%, 7.4% ± 6.4%, 12.4% ± 5.3% and 4.8% ± 3.0%. The between-subject CVs were 25.0%, 12.3%, 5.3%, 10.7% and 6.4%, respectively. Hippocampal long-TE sLASER single voxel spectroscopy can provide macromolecule-independent assessment of all major metabolites including Glx and m-Ins.
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Affiliation(s)
- Martin Gajdošík
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States
| | - Karl Landheer
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States
| | - Kelley M. Swanberg
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States
| | - Fatemeh Adlparvar
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
| | - Guillaume Madelin
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
| | - Wolfgang Bogner
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Christoph Juchem
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, United States
| | - Ivan I. Kirov
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, United States
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22
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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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23
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Clarke WT, Stagg CJ, Jbabdi S. FSL-MRS: An end-to-end spectroscopy analysis package. Magn Reson Med 2021; 85:2950-2964. [PMID: 33280161 PMCID: PMC7116822 DOI: 10.1002/mrm.28630] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/11/2020] [Accepted: 11/11/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE We introduce FSL-MRS, an end-to-end, modular, open-source MRS analysis toolbox. It provides spectroscopic data conversion, preprocessing, spectral simulation, fitting, quantitation, and visualization. METHODS The FSL-MRS package is modular. Its programs operate on data in a standard format (Neuroimaging Informatics Technology Initiative [NIfTI]) capable of storing single-voxel and multivoxel spectroscopy, including spatial orientation information. The FSL-MRS toolbox includes tools for preprocessing of raw spectroscopy data, including coil combination, frequency and phase alignment, and filtering. A density matrix simulation program is supplied for generation of basis spectra from simple text-based descriptions of pulse sequences. Fitting is based on linear combination of basis spectra and implements Markov chain Monte Carlo optimization for the estimation of the full posterior distribution of metabolite concentrations. Validation of the fitting is carried out on independently created simulated data, phantom data, and three in vivo human data sets (257 single-voxel spectroscopy and 8 MRSI data sets) at 3 T and 7 T. Interactive HTML reports are automatically generated by processing and fitting stages of the toolbox. The FSL-MRS package can be used on the command line or interactively in the Python language. RESULTS Validation of the fitting shows low error in simulation (median error of 11.9%) and in phantom (3.4%). Average correlation between a third-party toolbox (LCModel) and FSL-MRS was high (0.53-0.81) in all three in vivo data sets. CONCLUSION The FSL-MRS toolbox is designed to be flexible and extensible to new forms of spectroscopic acquisitions. Custom fitting models can be specified within the framework for dynamic or multivoxel spectroscopy. It is available as part of the FMRIB Software Library.
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Affiliation(s)
- William T. Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
- MRC Brain Network Dynamics UnitUniversity of OxfordOxfordUnited Kingdom
| | - Charlotte J. Stagg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
- MRC Brain Network Dynamics UnitUniversity of OxfordOxfordUnited Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
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24
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Marjańska M, Terpstra M. Influence of fitting approaches in LCModel on MRS quantification focusing on age-specific macromolecules and the spline baseline. NMR IN BIOMEDICINE 2021; 34:e4197. [PMID: 31782845 PMCID: PMC7255930 DOI: 10.1002/nbm.4197] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 08/20/2019] [Accepted: 09/10/2019] [Indexed: 05/17/2023]
Abstract
Quantification of neurochemical concentrations from 1 H MR spectra is challenged by incomplete knowledge of contributing signals. Some experimental conditions hinder the acquisition of artifact-free spectra and impede the acquisition of condition-specific macromolecule (MM) spectra. This work studies differences caused by fitting solutions routinely employed to manage resonances from MM and lipids. High quality spectra (free of residual water and lipid artifacts and for which condition-specific MM spectra are available) are used to understand the influences of spline baseline flexibility and noncondition-specific MM on neurochemical quantification. Fitting with moderate spline flexibility or using noncondition-specific MM led to quantification that differed from when an appropriate, fully specified model was used. This occurred for all neurochemicals to an extent that varied in magnitude among and within approaches. The spline baseline was more tortuous when less constrained and when used in combination with noncondition-specific MM. Increasing baseline flexibility did not reproduce concentrations quantified under appropriate conditions when spectra were fitted using a MM spectrum measured from a mismatched cohort. Using the noncondition-specific MM spectrum led to quantification differences that were comparable in size with using a fitting model that had moderate freedom, and these influences were additive. Although goodness of fit was better with greater fitting flexibility, quantification differed from when fitting with a fully specified model that is appropriate for low noise data. Notable GABA and PE concentration differences occurred with lower estimates of measurement error when fitting with greater spline flexibility or noncondition-specific MM. These data support the need for improved metrics of goodness of fit. Attempting to correct for artifacts or absence of a condition-specific MM spectrum via increased spline flexibility and usage of noncondition-specific MM spectra cannot replace artifact-free data quantified with a condition-specific MM spectrum.
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Affiliation(s)
- Małgorzata Marjańska
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, 2021 6 ST SE, Minneapolis, Minnesota 55455, United States
| | - Melissa Terpstra
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, 2021 6 ST SE, Minneapolis, Minnesota 55455, United States
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25
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High temporal resolution functional magnetic resonance spectroscopy in the mouse upon visual stimulation. Neuroimage 2021; 234:117973. [PMID: 33762216 DOI: 10.1016/j.neuroimage.2021.117973] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 03/11/2021] [Accepted: 03/12/2021] [Indexed: 12/18/2022] Open
Abstract
Functional magnetic resonance spectroscopy (fMRS) quantifies metabolic variations upon presentation of a stimulus and can therefore provide complementary information compared to activity inferred from functional magnetic resonance imaging (fMRI). Improving the temporal resolution of fMRS can be beneficial to clinical applications where detailed information on metabolism can assist the characterization of brain function in healthy and sick populations as well as for neuroscience applications where information on the nature of the underlying activity could be potentially gained. Furthermore, fMRS with higher temporal resolution could benefit basic studies on animal models of disease and for investigating brain function in general. However, to date, fMRS has been limited to sustained periods of activation which risk adaptation and other undesirable effects. Here, we performed fMRS experiments in the mouse with high temporal resolution (12 s), and show the feasibility of such an approach for reliably quantifying metabolic variations upon activation. We detected metabolic variations in the superior colliculus of mice subjected to visual stimulation delivered in a block paradigm at 9.4 T. A robust modulation of glutamate is observed on the average time course, on the difference spectra and on the concentration distributions during active and recovery periods. A general linear model is used for the statistical analysis, and for exploring the nature of the modulation. Changes in NAAG, PCr and Cr levels were also detected. A control experiment with no stimulation reveals potential metabolic signal "drifts" that are not correlated with the functional activity, which should be taken into account when analyzing fMRS data in general. Our findings are promising for future applications of fMRS.
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26
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Landheer K, Gajdošík M, Juchem C. A semi-LASER, single-voxel spectroscopic sequence with a minimal echo time of 20.1 ms in the human brain at 3 T. NMR IN BIOMEDICINE 2020; 33:e4324. [PMID: 32557880 DOI: 10.1002/nbm.4324] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 04/28/2020] [Accepted: 04/29/2020] [Indexed: 06/11/2023]
Abstract
An optimized semi-LASER sequence that is capable of acquiring artefact-free data with an echo time (TE) of 20.1 ms on a standard clinical 3 T MR system was developed. Simulations were performed to determine the optimal TEs that minimize the expected Cramér-Rao lower bound (CRLB) as proxy for quantification accuracy of metabolites. Optimized RF pulses, crusher gradients and phase cycling were used to achieve the shortest TE in a semi-LASER sequence to date on a clinical system. Synthetic spectra were simulated using the density matrix formalism for TEs spanning from 20.1 to 220.1 ms. These simulations were used to calculate the expected CRLB for each of the 18 metabolites typically considered in 1 H MRS. High quality spectra were obtained in six healthy volunteers in the prefrontal cortex, which is known for spurious echoes due to its proximity to the paranasal sinuses, and in the parietal-occipital cortex. Spectral transients were sufficient in quality to enable phase and frequency alignment prior to summation over all repetitions. Automated high-quality water suppression was obtained for all voxels without manual adjustment. The shortest TE minimized the CRLB for all brain metabolites except glycine due to its overlap with myo-inositol at this TE. It is also demonstrated that the CRLBs increase rapidly with TE for certain coupled metabolites.
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Affiliation(s)
- Karl Landheer
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, New York
| | - Martin Gajdošík
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, New York
| | - Christoph Juchem
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, New York
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, New York
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27
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Murali-Manohar S, Wright AM, Borbath T, Avdievich NI, Henning A. A novel method to measure T 1 -relaxation times of macromolecules and quantification of the macromolecular resonances. Magn Reson Med 2020; 85:601-614. [PMID: 32864826 DOI: 10.1002/mrm.28484] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 07/29/2020] [Accepted: 07/29/2020] [Indexed: 01/29/2023]
Abstract
PURPOSE Macromolecular peaks underlying metabolite spectra influence the quantification of metabolites. Therefore, it is important to understand the extent of contribution from macromolecules (MMs) in metabolite quantification. However, to model MMs more accurately in spectral fitting, differences in T1 relaxation times among individual MM peaks must be considered. Characterization of T1 -relaxation times for all individual MM peaks using a single inversion recovery technique is difficult due to eventual contributions from metabolites. On the contrary, a double inversion recovery (DIR) technique provided flexibility to acquire MM spectra spanning a range of longitudinal magnetizations with minimal metabolite influence. Thus, a novel method to determine T1 -relaxation times of individual MM peaks is reported in this work. METHODS Extensive Bloch simulations were performed to determine inversion time combinations for a DIR technique that yielded adequate MM signal with varying longitudinal magnetizations while minimizing metabolite contributions. MM spectra were acquired using DIR-metabolite-cycled semi-LASER sequence. LCModel concentrations were fitted to the DIR signal equation to calculate T1 -relaxation times. RESULTS T1 -relaxation times of MMs range from 204 to 510 ms and 253 to 564 ms in gray- and white-matter rich voxels respectively at 9.4T. Additionally, concentrations of 13 MM peaks are reported. CONCLUSION A novel DIR method is reported in this work to calculate T1 -relaxation times of MMs in the human brain. T1 -relaxation times and relaxation time corrected concentrations of individual MMs are reported in gray- and white-matter rich voxels for the first time at 9.4T.
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Affiliation(s)
- Saipavitra Murali-Manohar
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Andrew Martin Wright
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive & Systems Neuroscience, Tübingen, Germany
| | - Tamas Borbath
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Nikolai I Avdievich
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Anke Henning
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA
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28
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Wilson M. Adaptive baseline fitting for 1 H MR spectroscopy analysis. Magn Reson Med 2020; 85:13-29. [PMID: 32797656 DOI: 10.1002/mrm.28385] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 05/26/2020] [Accepted: 05/26/2020] [Indexed: 11/08/2022]
Abstract
PURPOSE Accurate baseline modeling is essential for reliable MRS analysis and interpretation-particularly at short echo-times, where enhanced metabolite information coincides with elevated baseline interference. The degree of baseline smoothness is a key analysis parameter for metabolite estimation, and in this study, a new method is presented to estimate its optimal value. METHODS An adaptive baseline fitting algorithm (ABfit) is described, incorporating a spline basis into a frequency-domain analysis model, with a penalty parameter to enforce baseline smoothness. A series of candidate analyses are performed over a range of smoothness penalties, as part of a 4-stage algorithm, and the Akaike information criterion is used to estimate the appropriate penalty. ABfit is applied to a set of simulated spectra with differing baseline features and experimentally acquired 2D MRSI-both at a field strength of 3 Tesla. RESULTS Simulated analyses demonstrate metabolite errors result from 2 main sources: bias from an inflexible baseline (underfitting) and increased variance from an overly flexible baseline (overfitting). In the case of an ideal flat baseline, ABfit is shown to correctly estimate a highly rigid baseline, and for more realistic spectra a reasonable compromise between bias and variance is found. Analysis of experimentally acquired data demonstrates good agreement with known correlations between metabolite ratios and the contributing volumes of gray and white matter tissue. CONCLUSIONS ABfit has been shown to perform accurate baseline estimation and is suitable for fully automated routine MRS analysis.
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Affiliation(s)
- Martin Wilson
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
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29
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An L, Araneta MF, Victorino M, Shen J. Determination of Brain Metabolite
T
1
Without Interference From Macromolecule Relaxation. J Magn Reson Imaging 2020; 52:1352-1359. [PMID: 32618104 PMCID: PMC10108383 DOI: 10.1002/jmri.27259] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 05/29/2020] [Accepted: 06/01/2020] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND J-coupled metabolites are often measured at a predetermined echo time in the presence of macromolecule signals, which complicates the measurement of metabolite T1 . PURPOSE To evaluate the feasibility and benefits of measuring metabolite T1 relaxation times without changing the overlapping macromolecule baseline signals. STUDY TYPE Prospective. SUBJECTS Five healthy volunteers (three females and two males; age = 27 ± 7 years). FIELD STRENGTH/SEQUENCE 7T scanner using a point resolved spectroscopy (PRESS)-based spectral editing MR spectroscopy (MRS) sequence with inversion recovery (IR). ASSESSMENT F-tests were performed to evaluate if the new approach, which fitted all the spectra together and used the same baselines for the three different IR settings, significantly reduced the variances of the metabolite T1 values compared to a conventional fitting approach. STATISTICAL TESTS Cramer-Rao lower bound (CRLB), within-subject coefficient of variation, and F-test. RESULTS The T1 relaxation times of N-acetylaspartate (NAA), total creatine (tCr), total choline (tCho), myo-inositol (mI), and glutamate (Glu) were determined with CRLB values below 6%. Glutamine (Gln) T1 was determined with a 17% CRLB, and the T1 of γ-aminobutyric acid (GABA) was determined with a 34% CRLB. The new approach significantly reduced the variances (F-test P < 0.05) of NAA, Glu, Gln, tCr, tCho, and mI T1 s compared to the conventional approach. DATA CONCLUSION Keeping macromolecule signals intact by using only long IR times allowed the use of a single macromolecule spectral model for different IR settings and significantly reduced the variances of NAA, Glu, Gln, tCr, tCho, and mI T1 s. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Li An
- Section on Magnetic Resonance Spectroscopy National Institute of Mental Health, National Institutes of Health Bethesda Maryland USA
| | - Maria Ferraris Araneta
- Section on Magnetic Resonance Spectroscopy National Institute of Mental Health, National Institutes of Health Bethesda Maryland USA
| | - Milalynn Victorino
- Section on Magnetic Resonance Spectroscopy National Institute of Mental Health, National Institutes of Health Bethesda Maryland USA
| | - Jun Shen
- Section on Magnetic Resonance Spectroscopy National Institute of Mental Health, National Institutes of Health Bethesda Maryland USA
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30
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Zhang Y, Shen J. Effects of noise and linewidth on in vivo analysis of glutamate at 3 T. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2020; 314:106732. [PMID: 32361510 PMCID: PMC8485252 DOI: 10.1016/j.jmr.2020.106732] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 03/24/2020] [Accepted: 04/11/2020] [Indexed: 05/17/2023]
Abstract
Magnetic resonance spectroscopy (MRS) can noninvasively detect metabolites in vivo, including glutamate (Glu). However, quantification is known to be affected by the overlaps among metabolite resonance lines and background macromolecule signals. We found that adding a moderate amount of noise or line broadening (2 Hz) caused large variations in concentration of Glu and other metabolites, when determined by LCModel analysis of in vivo short-echo time (TE) spectra. Theses variations were largely attributed to strong spectral baselines in short TE spectra, especially near 2.35 ppm, as well as overlapping metabolite resonance lines. To address this issue, we acquired in vivo data at 3 T using both short-TE and the multiple echo time J-resolved point-resolved spectroscopy (JPRESS) MRS techniques. We found that one-dimensional (1D) JPRESS, by simultaneously fitting the two cross-sections of JPRESS at J = 0 and J = 7.5 Hz, was highly resistant to variations in noise levels and spectral linewidths. Our results demonstrate that LCModel analysis of short-TE data is highly sensitive to variations in noise levels and spectral linewidths and this sensitivity is greatly reduced by 1D JPRESS given its substantially reduced baselines and enhanced spectral resolution.
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Affiliation(s)
- Yan Zhang
- MR Spectroscopy Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Jun Shen
- MR Spectroscopy Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA; Molecular Imaging Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
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Murali‐Manohar S, Borbath T, Wright AM, Soher B, Mekle R, Henning A. T
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relaxation times of macromolecules and metabolites in the human brain at 9.4 T. Magn Reson Med 2020; 84:542-558. [DOI: 10.1002/mrm.28174] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 12/06/2019] [Accepted: 12/27/2019] [Indexed: 11/10/2022]
Affiliation(s)
- Saipavitra Murali‐Manohar
- High‐Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tübingen Germany
- Faculty of Science University of Tübingen Tübingen Germany
| | - Tamas Borbath
- High‐Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tübingen Germany
- Faculty of Science University of Tübingen Tübingen Germany
| | - Andrew Martin Wright
- High‐Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tübingen Germany
- IMPRS for Cognitive & Systems Neuroscience Tübingen Germany
| | - Brian Soher
- Radiology Duke University Medical Center Durham North Carolina
| | - Ralf Mekle
- Center for Stroke Research Berlin (CSB) Charité ‐ Universitätsmedizin Berlin Berlin Germany
| | - Anke Henning
- High‐Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tübingen Germany
- Advanced Imaging Research Center UT Southwestern Medical Center Dallas Texas
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