Universal dynamic fitting of magnetic resonance spectroscopy.
MRS
dMRS
edited-MRS
fMRS
spectroscopy
Journal
Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245
Informations de publication
Date de publication:
24 Jan 2024
24 Jan 2024
Historique:
revised:
27
11
2023
received:
29
09
2023
accepted:
17
12
2023
medline:
24
1
2024
pubmed:
24
1
2024
entrez:
24
1
2024
Statut:
aheadofprint
Résumé
Dynamic (2D) MRS is a collection of techniques where acquisitions of spectra are repeated under varying experimental or physiological conditions. Dynamic MRS comprises a rich set of contrasts, including diffusion-weighted, relaxation-weighted, functional, edited, or hyperpolarized spectroscopy, leading to quantitative insights into multiple physiological or microstructural processes. Conventional approaches to dynamic MRS analysis ignore the shared information between spectra, and instead proceed by independently fitting noisy individual spectra before modeling temporal changes in the parameters. Here, we propose a universal dynamic MRS toolbox which allows simultaneous fitting of dynamic spectra of arbitrary type. A simple user-interface allows information to be shared and precisely modeled across spectra to make inferences on both spectral and dynamic processes. We demonstrate and thoroughly evaluate our approach in three types of dynamic MRS techniques. Simulations of functional and edited MRS are used to demonstrate the advantages of dynamic fitting. Analysis of synthetic functional A toolbox for generalized and universal fitting of dynamic, interrelated MR spectra has been released and validated. The toolbox is shared as a fully open-source software with comprehensive documentation, example data, and tutorials.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Wellcome Trust
ID : 203139/A/16/Z
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 203139/Z/16/Z
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 215573/Z/19/Z
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 221933/Z/20/Z
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 225924/Z/22/Z
Pays : United Kingdom
Informations de copyright
© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.
Références
Craven AR, Dwyer G, Ersland L, et al. GABA, glutamatergic dynamics and BOLD contrast concurrently assessed using functional MR spectroscopy during a cognitive task. bioRxiv. 2023;2023:539017. doi:10.1101/2023.05.02.539017
Hui SCN, Mikkelsen M, Zöllner HJ, et al. Frequency drift in MR spectroscopy at 3T. Neuroimage. 2021;241:118430. doi:10.1016/j.neuroimage.2021.118430
Edden RAE, Puts NAJ, Harris AD, Barker PB, Evans CJ. Gannet: A Batch-Processing Tool for the Quantitative Analysis of Gamma-Aminobutyric Acid-Edited MR Spectroscopy Spectra. J Magn Reson Imaging JMRI. 2014;40:1445. doi:10.1002/jmri.24478
Near J, Edden R, Evans CJ, Paquin R, Harris A, Jezzard P. Frequency and phase drift correction of magnetic resonance spectroscopy data by spectral registration in the time domain. Magn Reson Med. 2015;73:44-50. doi:10.1002/mrm.25094
Stanley JA, Raz N. Functional magnetic resonance spectroscopy: the “new” MRS for cognitive neuroscience and psychiatry research. Front Psych. 2018;9:76. doi:10.3389/fpsyt.2018.00076
Ligneul C, Najac C, Döring A, et al. Diffusion-weighted MR spectroscopy: consensus, recommendations, and resources from acquisition to modeling. Magn Reson Med. 2023;91:860-885. doi:10.1002/mrm.29877
Choi IY, Andronesi OC, Barker P, et al. Spectral editing in 1H magnetic resonance spectroscopy: Experts' consensus recommendations. NMR Biomed. 2021;34:e4411. doi:10.1002/nbm.4411
Mullins PG, McGonigle DJ, O'Gorman RL, et al. Current practice in the use of MEGA-PRESS spectroscopy for the detection of GABA. Neuroimage. 2014;86:43-52. doi:10.1016/j.neuroimage.2012.12.004
Ronen I, Valette J. Diffusion-weighted magnetic resonance spectroscopy. eMagRes. John Wiley & Sons, Ltd; 2015:733-750.
Palombo M, Shemesh N, Ronen I, Valette J. Insights into brain microstructure from in vivo DW-MRS. Neuroimage. 2018;182:97-116. doi:10.1016/j.neuroimage.2017.11.028
Koush Y, Rothman DL, Behar KL, de Graaf RA, Hyder F. Human brain functional MRS reveals interplay of metabolites implicated in neurotransmission and neuroenergetics. J Cereb Blood Flow Metab. 2022;42:911-934. doi:10.1177/0271678X221076570
Bednařík P, Tkáč I, Giove F, et al. Neurochemical and BOLD responses during neuronal activation measured in the human visual cortex at 7 tesla. J Cereb Blood Flow Metab. 2015;35:601-610. doi:10.1038/jcbfm.2014.233
Valkovič L, Chmelík M, Krššák M. In-vivo 31P-MRS of skeletal muscle and liver: a way for non-invasive assessment of their metabolism. Anal Biochem. 2017;529:193-215. doi:10.1016/j.ab.2017.01.018
Wang T, Zhu X, Li H, et al. Noninvasive assessment of myocardial energy metabolism and dynamics using in vivo deuterium MRS imaging. Magn Reson Med. 2021;86:2899-2909. doi:10.1002/mrm.28914
Hurd RE, Yen Y, Chen A, Ardenkjaer-Larsen JH. Hyperpolarized 13 C metabolic imaging using dissolution dynamic nuclear polarization. J Magn Reson Imaging. 2012;36:1314-1328. doi:10.1002/jmri.23753
Chhina N, Kuestermann E, Halliday J, et al. Measurement of human tricarboxylic acid cycle rates during visual activation by 13 C magnetic resonance spectroscopy. J Neurosci Res. 2001;66:737-746. doi:10.1002/jnr.10053
Kulpanovich A, Tal A. The application of magnetic resonance fingerprinting to single voxel proton spectroscopy. NMR Biomed. 2018;31:e4001. doi:10.1002/nbm.4001
Hoefemann M, Bolliger CS, Chong DGQ, van der Veen JW, Kreis R. Parameterization of metabolite and macromolecule contributions in interrelated MR spectra of human brain using multidimensional modeling. NMR Biomed. 2020;33:e4328. doi:10.1002/nbm.4328
Kirov II, Tal A. Potential clinical impact of multiparametric quantitative MR spectroscopy in neurological disorders: a review and analysis. Magn Reson Med. 2020;83:22-44. doi:10.1002/mrm.27912
Gudmundson AT, Koo A, Virovka A, et al. Meta-analysis and open-source database for in vivo brain magnetic resonance spectroscopy in health and disease. Anal Biochem. 2023;676:115227. doi:10.1016/j.ab.2023.115227
Kiefer AP, Govindaraju V, Matson GB, Weiner MW, Maudsley AA. Multiple-echo proton spectroscopic imaging using time domain parametric spectral analysis. Magn Reson Med. 1998;39:528-538. doi:10.1002/mrm.1910390405
Bolliger CS, Boesch C, Kreis R. On the use of Cramér-Rao minimum variance bounds for the design of magnetic resonance spectroscopy experiments. Neuroimage. 2013;83:1031-1040. doi:10.1016/j.neuroimage.2013.07.062
Kreis R, Slotboom J, Hofmann L, Boesch C. Integrated data acquisition and processing to determine metabolite contents, relaxation times, and macromolecule baseline in single examinations of individual subjects. Magn Reson Med. 2005;54:761-768. doi:10.1002/mrm.20673
Chong DGQ, Kreis R, Bolliger CS, Boesch C, Slotboom J. Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a fitting tool for arrays of interrelated datasets. Magn Reson Mater Phys Biol Med. 2011;24:147-164. doi:10.1007/s10334-011-0246-y
Schulte RF, Boesiger P. ProFit: two-dimensional prior-knowledge fitting of J-resolved spectra. NMR Biomed. 2006;19:255-263. doi:10.1002/nbm.1026
Gonenc A, Govind V, Sheriff S, Maudsley AA. Comparison of spectral fitting methods for overlapping J-coupled metabolite resonances. Magn Reson Med. 2010;64:623-628. doi:10.1002/mrm.22540
Vanhamme L, Van Huffel S, Van Hecke P, van Ormondt D. Time-domain quantification of series of biomedical magnetic resonance spectroscopy signals. J Magn Reson. 1999;140:120-130. doi:10.1006/jmre.1999.1835
An L, Araneta MF, Johnson C, Shen J. Simultaneous measurement of glutamate, glutamine, GABA, and glutathione by spectral editing without subtraction. Magn Reson Med. 2018;80:1776-1786. doi:10.1002/mrm.27172
Adalid V, Döring A, Kyathanahally SP, Bolliger CS, Boesch C, Kreis R. Fitting interrelated datasets: metabolite diffusion and general lineshapes. Magn Reson Mater Phys Biol Med. 2017;30:429-448. doi:10.1007/s10334-017-0618-z
Şimşek K, Döring A, Pampel A, Möller HE, Kreis R. Macromolecular background signal and non-Gaussian metabolite diffusion determined in human brain using ultra-high diffusion weighting. Magn Reson Med. 2022;88:1962-1977. doi:10.1002/mrm.29367
Rizzo R, Kreis R. Multi-echo single-shot spectroscopy combined with simultaneous 2D model fitting for fast and accurate measurement of metabolite-specific concentrations and T2 relaxation times. NMR Biomed. 2023;36:e5016. doi:10.1002/nbm.5016
Clarke WT, Stagg CJ, Jbabdi S. FSL-MRS: An end-to-end spectroscopy analysis package. Magn Reson Med. 2021;85:2950-2964. doi:10.1002/mrm.28630
Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. NeuroImage. 2012;62:782-790. doi:10.1016/j.neuroimage.2011.09.015
Tal A. The future is 2D: spectral-temporal fitting of dynamic MRS data provides exponential gains in precision over conventional approaches. Magn Reson Med. 2023;89:499-507. doi:10.1002/mrm.29456
Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med. 1993;30:672-679. doi:10.1002/mrm.1910300604
Woolrich MW, Behrens TEJ, Beckmann CF, Jenkinson M, Smith SM. Multilevel linear modelling for FMRI group analysis using Bayesian inference. Neuroimage. 2004;21:1732-1747. doi:10.1016/j.neuroimage.2003.12.023
Clarke WT, Jbabdi S. FSL-MRS. Zenodo; 2022. doi:10.5281/zenodo.7956237
Clarke WT. Universal Dynamic Fitting of Magnetic Resonance Spectroscopy-Source Code. 2023. doi:10.5281/zenodo.7956321
Clarke WT. Universal Dynamic Fitting of Magnetic Resonance Spectroscopy-Data. 2023. doi:10.5281/zenodo.7950984
Juchem C, Cudalbu C, de Graaf RA, et al. B0 shimming for in vivo magnetic resonance spectroscopy: Experts' consensus recommendations. NMR Biomed. 2021;34:e4350. doi:10.1002/nbm.4350
Ernst RR, Bodenhausen G, Wokaun A. Principles of Nuclear Magnetic Resonance in One and Two Dimensions. Clarendon Press; 2004.
GLM-FslWiki. https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM. (accessed: November 15, 2023)
Clarke WT. wtclarke/fsl_mrs_fmrs_demo: FSL-MRS dynamic fitting-preprint. 2023. doi:10.5281/zenodo.7951649
Ip IB, Berrington A, Hess AT, Parker AJ, Emir UE, Bridge H. Combined fMRI-MRS acquires simultaneous glutamate and BOLD-fMRI signals in the human brain. Neuroimage. 2017;155:113-119. doi:10.1016/j.neuroimage.2017.04.030
Abraham A, Pedregosa F, Eickenberg M, et al. Machine learning for neuroimaging with scikit-learn. Front Neuroinform. 2014;8:14. doi:10.3389/fninf.2014.00014
Glover GH. Deconvolution of impulse response in event-related BOLD fMRI. Neuroimage. 1999;9:416-429. doi:10.1006/nimg.1998.0419
Jbabdi S, Sotiropoulos SN, Savio AM, Graña M, Behrens TEJ. Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems. Magn Reson Med. 2012;68:1846-1855. doi:10.1002/mrm.24204
Ligneul C, Palombo M, Hernández-Garzón E, et al. Diffusion-weighted magnetic resonance spectroscopy enables cell-specific monitoring of astrocyte reactivity in vivo. Neuroimage. 2019;191:457-469. doi:10.1016/j.neuroimage.2019.02.046
Ligneul C, Palombo M, Valette J. Metabolite diffusion up to very high b in the mouse brain in vivo: revisiting the potential correlation between relaxation and diffusion properties. Magn Reson Med. 2017;77:1390-1398. doi:10.1002/mrm.26217
Novikov DS, Kiselev VG, Jespersen SN. On modeling. Magn Reson Med. 2018;79:3172-3193. doi:10.1002/mrm.27101
Koolschijn RS, Clarke WT, Ip IB, Emir UE, Barron HC. Event-related functional magnetic resonance spectroscopy. Neuroimage. 2023;276:120194. doi:10.1016/j.neuroimage.2023.120194
Clarke WT, Bell TK, Emir UE, et al. NIfTI-MRS: a standard data format for magnetic resonance spectroscopy. Magn Reson Med. 2022;88:2358-2370. doi:10.1002/mrm.29418
Cox RW, Ashburner J, Breman H, et al. A (Sort of) New Image Data Format Standard: NiFTI-1. 2004.