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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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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. |
format | Online Article Text |
id | pubmed-7116822 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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