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Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging

Multi-shell and diffusion spectrum imaging (DSI) are becoming increasingly popular methods of acquiring diffusion MRI data in a research context. However, single-shell acquisitions, such as diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI), still remain the most co...

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Autores principales: Yeh, Fang-Cheng, Verstynen, Timothy D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5021685/
https://www.ncbi.nlm.nih.gov/pubmed/27683539
http://dx.doi.org/10.3389/fnins.2016.00418
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author Yeh, Fang-Cheng
Verstynen, Timothy D.
author_facet Yeh, Fang-Cheng
Verstynen, Timothy D.
author_sort Yeh, Fang-Cheng
collection PubMed
description Multi-shell and diffusion spectrum imaging (DSI) are becoming increasingly popular methods of acquiring diffusion MRI data in a research context. However, single-shell acquisitions, such as diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI), still remain the most common acquisition schemes in practice. Here we tested whether multi-shell and DSI data have conversion flexibility to be interpolated into corresponding HARDI data. We acquired multi-shell and DSI data on both a phantom and in vivo human tissue and converted them to HARDI. The correlation and difference between their diffusion signals, anisotropy values, diffusivity measurements, fiber orientations, connectivity matrices, and network measures were examined. Our analysis result showed that the diffusion signals, anisotropy, diffusivity, and connectivity matrix of the HARDI converted from multi-shell and DSI were highly correlated with those of the HARDI acquired on the MR scanner, with correlation coefficients around 0.8~0.9. The average angular error between converted and original HARDI was 20.7° at voxels with signal-to-noise ratios greater than 5. The network topology measures had less than 2% difference, whereas the average nodal measures had a percentage difference around 4~7%. In general, multi-shell and DSI acquisitions can be converted to their corresponding single-shell HARDI with high fidelity. This supports multi-shell and DSI acquisitions over HARDI acquisition as the scheme of choice for diffusion acquisitions.
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spelling pubmed-50216852016-09-28 Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging Yeh, Fang-Cheng Verstynen, Timothy D. Front Neurosci Neuroscience Multi-shell and diffusion spectrum imaging (DSI) are becoming increasingly popular methods of acquiring diffusion MRI data in a research context. However, single-shell acquisitions, such as diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI), still remain the most common acquisition schemes in practice. Here we tested whether multi-shell and DSI data have conversion flexibility to be interpolated into corresponding HARDI data. We acquired multi-shell and DSI data on both a phantom and in vivo human tissue and converted them to HARDI. The correlation and difference between their diffusion signals, anisotropy values, diffusivity measurements, fiber orientations, connectivity matrices, and network measures were examined. Our analysis result showed that the diffusion signals, anisotropy, diffusivity, and connectivity matrix of the HARDI converted from multi-shell and DSI were highly correlated with those of the HARDI acquired on the MR scanner, with correlation coefficients around 0.8~0.9. The average angular error between converted and original HARDI was 20.7° at voxels with signal-to-noise ratios greater than 5. The network topology measures had less than 2% difference, whereas the average nodal measures had a percentage difference around 4~7%. In general, multi-shell and DSI acquisitions can be converted to their corresponding single-shell HARDI with high fidelity. This supports multi-shell and DSI acquisitions over HARDI acquisition as the scheme of choice for diffusion acquisitions. Frontiers Media S.A. 2016-09-14 /pmc/articles/PMC5021685/ /pubmed/27683539 http://dx.doi.org/10.3389/fnins.2016.00418 Text en Copyright © 2016 Yeh and Verstynen. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Yeh, Fang-Cheng
Verstynen, Timothy D.
Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging
title Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging
title_full Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging
title_fullStr Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging
title_full_unstemmed Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging
title_short Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging
title_sort converting multi-shell and diffusion spectrum imaging to high angular resolution diffusion imaging
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5021685/
https://www.ncbi.nlm.nih.gov/pubmed/27683539
http://dx.doi.org/10.3389/fnins.2016.00418
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