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Constrained spherical deconvolution of nonspherically sampled diffusion MRI data

Constrained spherical deconvolution (CSD) of diffusion‐weighted MRI (DW‐MRI) is a popular analysis method that extracts the full white matter (WM) fiber orientation density function (fODF) in the living human brain, noninvasively. It assumes that the DW‐MRI signal on the sphere can be represented as...

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Autores principales: Morez, Jan, Sijbers, Jan, Vanhevel, Floris, Jeurissen, Ben
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7776001/
https://www.ncbi.nlm.nih.gov/pubmed/33169880
http://dx.doi.org/10.1002/hbm.25241
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author Morez, Jan
Sijbers, Jan
Vanhevel, Floris
Jeurissen, Ben
author_facet Morez, Jan
Sijbers, Jan
Vanhevel, Floris
Jeurissen, Ben
author_sort Morez, Jan
collection PubMed
description Constrained spherical deconvolution (CSD) of diffusion‐weighted MRI (DW‐MRI) is a popular analysis method that extracts the full white matter (WM) fiber orientation density function (fODF) in the living human brain, noninvasively. It assumes that the DW‐MRI signal on the sphere can be represented as the spherical convolution of a single‐fiber response function (RF) and the fODF, and recovers the fODF through the inverse operation. CSD approaches typically require that the DW‐MRI data is sampled shell‐wise, and estimate the RF in a purely spherical manner using spherical basis functions, such as spherical harmonics (SH), disregarding any radial dependencies. This precludes analysis of data acquired with nonspherical sampling schemes, for example, Cartesian sampling. Additionally, nonspherical sampling can also arise due to technical issues, for example, gradient nonlinearities, resulting in a spatially dependent bias of the apparent tissue densities and connectivity information. Here, we adopt a compact model for the RFs that also describes their radial dependency. We demonstrate that the proposed model can accurately predict the tissue response for a wide range of b‐values. On shell‐wise data, our approach provides fODFs and tissue densities indistinguishable from those estimated using SH. On Cartesian data, fODF estimates and apparent tissue densities are on par with those obtained from shell‐wise data, significantly broadening the range of data sets that can be analyzed using CSD. In addition, gradient nonlinearities can be accounted for using the proposed model, resulting in much more accurate apparent tissue densities and connectivity metrics.
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spelling pubmed-77760012021-01-07 Constrained spherical deconvolution of nonspherically sampled diffusion MRI data Morez, Jan Sijbers, Jan Vanhevel, Floris Jeurissen, Ben Hum Brain Mapp Research Articles Constrained spherical deconvolution (CSD) of diffusion‐weighted MRI (DW‐MRI) is a popular analysis method that extracts the full white matter (WM) fiber orientation density function (fODF) in the living human brain, noninvasively. It assumes that the DW‐MRI signal on the sphere can be represented as the spherical convolution of a single‐fiber response function (RF) and the fODF, and recovers the fODF through the inverse operation. CSD approaches typically require that the DW‐MRI data is sampled shell‐wise, and estimate the RF in a purely spherical manner using spherical basis functions, such as spherical harmonics (SH), disregarding any radial dependencies. This precludes analysis of data acquired with nonspherical sampling schemes, for example, Cartesian sampling. Additionally, nonspherical sampling can also arise due to technical issues, for example, gradient nonlinearities, resulting in a spatially dependent bias of the apparent tissue densities and connectivity information. Here, we adopt a compact model for the RFs that also describes their radial dependency. We demonstrate that the proposed model can accurately predict the tissue response for a wide range of b‐values. On shell‐wise data, our approach provides fODFs and tissue densities indistinguishable from those estimated using SH. On Cartesian data, fODF estimates and apparent tissue densities are on par with those obtained from shell‐wise data, significantly broadening the range of data sets that can be analyzed using CSD. In addition, gradient nonlinearities can be accounted for using the proposed model, resulting in much more accurate apparent tissue densities and connectivity metrics. John Wiley & Sons, Inc. 2020-11-10 /pmc/articles/PMC7776001/ /pubmed/33169880 http://dx.doi.org/10.1002/hbm.25241 Text en © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Research Articles
Morez, Jan
Sijbers, Jan
Vanhevel, Floris
Jeurissen, Ben
Constrained spherical deconvolution of nonspherically sampled diffusion MRI data
title Constrained spherical deconvolution of nonspherically sampled diffusion MRI data
title_full Constrained spherical deconvolution of nonspherically sampled diffusion MRI data
title_fullStr Constrained spherical deconvolution of nonspherically sampled diffusion MRI data
title_full_unstemmed Constrained spherical deconvolution of nonspherically sampled diffusion MRI data
title_short Constrained spherical deconvolution of nonspherically sampled diffusion MRI data
title_sort constrained spherical deconvolution of nonspherically sampled diffusion mri data
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7776001/
https://www.ncbi.nlm.nih.gov/pubmed/33169880
http://dx.doi.org/10.1002/hbm.25241
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