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CACTUS: a computational framework for generating realistic white matter microstructure substrates

Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in controlled environments. This is fundamental for understanding the link between micrometre-scale tissue properties and DW...

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Autores principales: Villarreal-Haro, Juan Luis, Gardier, Remy, Canales-Rodríguez, Erick J., Fischi-Gomez, Elda, Girard, Gabriel, Thiran, Jean-Philippe, Rafael-Patiño, Jonathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10434236/
https://www.ncbi.nlm.nih.gov/pubmed/37603781
http://dx.doi.org/10.3389/fninf.2023.1208073
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author Villarreal-Haro, Juan Luis
Gardier, Remy
Canales-Rodríguez, Erick J.
Fischi-Gomez, Elda
Girard, Gabriel
Thiran, Jean-Philippe
Rafael-Patiño, Jonathan
author_facet Villarreal-Haro, Juan Luis
Gardier, Remy
Canales-Rodríguez, Erick J.
Fischi-Gomez, Elda
Girard, Gabriel
Thiran, Jean-Philippe
Rafael-Patiño, Jonathan
author_sort Villarreal-Haro, Juan Luis
collection PubMed
description Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in controlled environments. This is fundamental for understanding the link between micrometre-scale tissue properties and DW-MRI signals measured at the millimetre-scale, optimizing acquisition protocols to target microstructure properties of interest, and exploring the robustness and accuracy of estimation methods. However, accurate simulations require substrates that reflect the main microstructural features of the studied tissue. To address this challenge, we introduce a novel computational workflow, CACTUS (Computational Axonal Configurator for Tailored and Ultradense Substrates), for generating synthetic white matter substrates. Our approach allows constructing substrates with higher packing density than existing methods, up to 95% intra-axonal volume fraction, and larger voxel sizes of up to 500μm(3) with rich fibre complexity. CACTUS generates bundles with angular dispersion, bundle crossings, and variations along the fibres of their inner and outer radii and g-ratio. We achieve this by introducing a novel global cost function and a fibre radial growth approach that allows substrates to match predefined targeted characteristics and mirror those reported in histological studies. CACTUS improves the development of complex synthetic substrates, paving the way for future applications in microstructure imaging.
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spelling pubmed-104342362023-08-18 CACTUS: a computational framework for generating realistic white matter microstructure substrates Villarreal-Haro, Juan Luis Gardier, Remy Canales-Rodríguez, Erick J. Fischi-Gomez, Elda Girard, Gabriel Thiran, Jean-Philippe Rafael-Patiño, Jonathan Front Neuroinform Neuroscience Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in controlled environments. This is fundamental for understanding the link between micrometre-scale tissue properties and DW-MRI signals measured at the millimetre-scale, optimizing acquisition protocols to target microstructure properties of interest, and exploring the robustness and accuracy of estimation methods. However, accurate simulations require substrates that reflect the main microstructural features of the studied tissue. To address this challenge, we introduce a novel computational workflow, CACTUS (Computational Axonal Configurator for Tailored and Ultradense Substrates), for generating synthetic white matter substrates. Our approach allows constructing substrates with higher packing density than existing methods, up to 95% intra-axonal volume fraction, and larger voxel sizes of up to 500μm(3) with rich fibre complexity. CACTUS generates bundles with angular dispersion, bundle crossings, and variations along the fibres of their inner and outer radii and g-ratio. We achieve this by introducing a novel global cost function and a fibre radial growth approach that allows substrates to match predefined targeted characteristics and mirror those reported in histological studies. CACTUS improves the development of complex synthetic substrates, paving the way for future applications in microstructure imaging. Frontiers Media S.A. 2023-08-01 /pmc/articles/PMC10434236/ /pubmed/37603781 http://dx.doi.org/10.3389/fninf.2023.1208073 Text en Copyright © 2023 Villarreal-Haro, Gardier, Canales-Rodríguez, Fischi-Gomez, Girard, Thiran and Rafael-Patiño. 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
Villarreal-Haro, Juan Luis
Gardier, Remy
Canales-Rodríguez, Erick J.
Fischi-Gomez, Elda
Girard, Gabriel
Thiran, Jean-Philippe
Rafael-Patiño, Jonathan
CACTUS: a computational framework for generating realistic white matter microstructure substrates
title CACTUS: a computational framework for generating realistic white matter microstructure substrates
title_full CACTUS: a computational framework for generating realistic white matter microstructure substrates
title_fullStr CACTUS: a computational framework for generating realistic white matter microstructure substrates
title_full_unstemmed CACTUS: a computational framework for generating realistic white matter microstructure substrates
title_short CACTUS: a computational framework for generating realistic white matter microstructure substrates
title_sort cactus: a computational framework for generating realistic white matter microstructure substrates
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10434236/
https://www.ncbi.nlm.nih.gov/pubmed/37603781
http://dx.doi.org/10.3389/fninf.2023.1208073
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