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Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging
Compressed sensing (CS) is widely used to accelerate clinical diffusion MRI acquisitions, but it is not widely used in preclinical settings yet. In this study, we optimized and compared several CS reconstruction methods for diffusion imaging. Different undersampling patterns and two reconstruction a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272537/ https://www.ncbi.nlm.nih.gov/pubmed/37332879 http://dx.doi.org/10.3389/fnins.2023.1172830 |
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author | de Souza, Diego Alves Rodrigues Mathieu, Hervé Deloulme, Jean-Christophe Barbier, Emmanuel L. |
author_facet | de Souza, Diego Alves Rodrigues Mathieu, Hervé Deloulme, Jean-Christophe Barbier, Emmanuel L. |
author_sort | de Souza, Diego Alves Rodrigues |
collection | PubMed |
description | Compressed sensing (CS) is widely used to accelerate clinical diffusion MRI acquisitions, but it is not widely used in preclinical settings yet. In this study, we optimized and compared several CS reconstruction methods for diffusion imaging. Different undersampling patterns and two reconstruction approaches were evaluated: conventional CS, based on Berkeley Advanced Reconstruction Toolbox (BART-CS) toolbox, and a new kernel low-rank (KLR)-CS, based on kernel principal component analysis and low-resolution-phase (LRP) maps. 3D CS acquisitions were performed at 9.4T using a 4-element cryocoil on mice (wild type and a MAP6 knockout). Comparison metrics were error and structural similarity index measure (SSIM) on fractional anisotropy (FA) and mean diffusivity (MD), as well as reconstructions of the anterior commissure and fornix. Acceleration factors (AF) up to 6 were considered. In the case of retrospective undersampling, the proposed KLR-CS outperformed BART-CS up to AF = 6 for FA and MD maps and tractography. For instance, for AF = 4, the maximum errors were, respectively, 8.0% for BART-CS and 4.9% for KLR-CS, considering both FA and MD in the corpus callosum. Regarding undersampled acquisitions, these maximum errors became, respectively, 10.5% for BART-CS and 7.0% for KLR-CS. This difference between simulations and acquisitions arose mainly from repetition noise, but also from differences in resonance frequency drift, signal-to-noise ratio, and in reconstruction noise. Despite this increased error, fully sampled and AF = 2 yielded comparable results for FA, MD and tractography, and AF = 4 showed minor faults. Altogether, KLR-CS based on LRP maps seems a robust approach to accelerate preclinical diffusion MRI and thereby limit the effect of the frequency drift. |
format | Online Article Text |
id | pubmed-10272537 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102725372023-06-17 Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging de Souza, Diego Alves Rodrigues Mathieu, Hervé Deloulme, Jean-Christophe Barbier, Emmanuel L. Front Neurosci Neuroscience Compressed sensing (CS) is widely used to accelerate clinical diffusion MRI acquisitions, but it is not widely used in preclinical settings yet. In this study, we optimized and compared several CS reconstruction methods for diffusion imaging. Different undersampling patterns and two reconstruction approaches were evaluated: conventional CS, based on Berkeley Advanced Reconstruction Toolbox (BART-CS) toolbox, and a new kernel low-rank (KLR)-CS, based on kernel principal component analysis and low-resolution-phase (LRP) maps. 3D CS acquisitions were performed at 9.4T using a 4-element cryocoil on mice (wild type and a MAP6 knockout). Comparison metrics were error and structural similarity index measure (SSIM) on fractional anisotropy (FA) and mean diffusivity (MD), as well as reconstructions of the anterior commissure and fornix. Acceleration factors (AF) up to 6 were considered. In the case of retrospective undersampling, the proposed KLR-CS outperformed BART-CS up to AF = 6 for FA and MD maps and tractography. For instance, for AF = 4, the maximum errors were, respectively, 8.0% for BART-CS and 4.9% for KLR-CS, considering both FA and MD in the corpus callosum. Regarding undersampled acquisitions, these maximum errors became, respectively, 10.5% for BART-CS and 7.0% for KLR-CS. This difference between simulations and acquisitions arose mainly from repetition noise, but also from differences in resonance frequency drift, signal-to-noise ratio, and in reconstruction noise. Despite this increased error, fully sampled and AF = 2 yielded comparable results for FA, MD and tractography, and AF = 4 showed minor faults. Altogether, KLR-CS based on LRP maps seems a robust approach to accelerate preclinical diffusion MRI and thereby limit the effect of the frequency drift. Frontiers Media S.A. 2023-06-02 /pmc/articles/PMC10272537/ /pubmed/37332879 http://dx.doi.org/10.3389/fnins.2023.1172830 Text en Copyright © 2023 de Souza, Mathieu, Deloulme and Barbier. https://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) and the copyright owner(s) 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 de Souza, Diego Alves Rodrigues Mathieu, Hervé Deloulme, Jean-Christophe Barbier, Emmanuel L. Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging |
title | Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging |
title_full | Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging |
title_fullStr | Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging |
title_full_unstemmed | Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging |
title_short | Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging |
title_sort | evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272537/ https://www.ncbi.nlm.nih.gov/pubmed/37332879 http://dx.doi.org/10.3389/fnins.2023.1172830 |
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