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FSL‐MRS: An end‐to‐end spectroscopy analysis package

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...

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Detalles Bibliográficos
Autores principales: Clarke, William T., Stagg, Charlotte J., Jbabdi, Saad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7116822/
https://www.ncbi.nlm.nih.gov/pubmed/33280161
http://dx.doi.org/10.1002/mrm.28630
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author Clarke, William T.
Stagg, Charlotte J.
Jbabdi, Saad
author_facet Clarke, William T.
Stagg, Charlotte J.
Jbabdi, Saad
author_sort Clarke, William T.
collection PubMed
description 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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spelling pubmed-71168222021-03-24 FSL‐MRS: An end‐to‐end spectroscopy analysis package Clarke, William T. Stagg, Charlotte J. Jbabdi, Saad Magn Reson Med Full Papers—Spectroscopic Methodology 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. John Wiley and Sons Inc. 2020-12-06 2021-06 /pmc/articles/PMC7116822/ /pubmed/33280161 http://dx.doi.org/10.1002/mrm.28630 Text en © 2020 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Full Papers—Spectroscopic Methodology
Clarke, William T.
Stagg, Charlotte J.
Jbabdi, Saad
FSL‐MRS: An end‐to‐end spectroscopy analysis package
title FSL‐MRS: An end‐to‐end spectroscopy analysis package
title_full FSL‐MRS: An end‐to‐end spectroscopy analysis package
title_fullStr FSL‐MRS: An end‐to‐end spectroscopy analysis package
title_full_unstemmed FSL‐MRS: An end‐to‐end spectroscopy analysis package
title_short FSL‐MRS: An end‐to‐end spectroscopy analysis package
title_sort fsl‐mrs: an end‐to‐end spectroscopy analysis package
topic Full Papers—Spectroscopic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7116822/
https://www.ncbi.nlm.nih.gov/pubmed/33280161
http://dx.doi.org/10.1002/mrm.28630
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