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Manifold-aware synthesis of high-resolution diffusion from structural imaging
The physical and clinical constraints surrounding diffusion-weighted imaging (DWI) often limit the spatial resolution of the produced images to voxels up to eight times larger than those of T1w images. The detailed information contained in accessible high-resolution T1w images could help in the synt...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406190/ https://www.ncbi.nlm.nih.gov/pubmed/37555146 http://dx.doi.org/10.3389/fnimg.2022.930496 |
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author | Anctil-Robitaille, Benoit Théberge, Antoine Jodoin, Pierre-Marc Descoteaux, Maxime Desrosiers, Christian Lombaert, Hervé |
author_facet | Anctil-Robitaille, Benoit Théberge, Antoine Jodoin, Pierre-Marc Descoteaux, Maxime Desrosiers, Christian Lombaert, Hervé |
author_sort | Anctil-Robitaille, Benoit |
collection | PubMed |
description | The physical and clinical constraints surrounding diffusion-weighted imaging (DWI) often limit the spatial resolution of the produced images to voxels up to eight times larger than those of T1w images. The detailed information contained in accessible high-resolution T1w images could help in the synthesis of diffusion images with a greater level of detail. However, the non-Euclidean nature of diffusion imaging hinders current deep generative models from synthesizing physically plausible images. In this work, we propose the first Riemannian network architecture for the direct generation of diffusion tensors (DT) and diffusion orientation distribution functions (dODFs) from high-resolution T1w images. Our integration of the log-Euclidean Metric into a learning objective guarantees, unlike standard Euclidean networks, the mathematically-valid synthesis of diffusion. Furthermore, our approach improves the fractional anisotropy mean squared error (FA MSE) between the synthesized diffusion and the ground-truth by more than 23% and the cosine similarity between principal directions by almost 5% when compared to our baselines. We validate our generated diffusion by comparing the resulting tractograms to our expected real data. We observe similar fiber bundles with streamlines having <3% difference in length, <1% difference in volume, and a visually close shape. While our method is able to generate diffusion images from structural inputs in a high-resolution space within 15 s, we acknowledge and discuss the limits of diffusion inference solely relying on T1w images. Our results nonetheless suggest a relationship between the high-level geometry of the brain and its overall white matter architecture that remains to be explored. |
format | Online Article Text |
id | pubmed-10406190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104061902023-08-08 Manifold-aware synthesis of high-resolution diffusion from structural imaging Anctil-Robitaille, Benoit Théberge, Antoine Jodoin, Pierre-Marc Descoteaux, Maxime Desrosiers, Christian Lombaert, Hervé Front Neuroimaging Neuroimaging The physical and clinical constraints surrounding diffusion-weighted imaging (DWI) often limit the spatial resolution of the produced images to voxels up to eight times larger than those of T1w images. The detailed information contained in accessible high-resolution T1w images could help in the synthesis of diffusion images with a greater level of detail. However, the non-Euclidean nature of diffusion imaging hinders current deep generative models from synthesizing physically plausible images. In this work, we propose the first Riemannian network architecture for the direct generation of diffusion tensors (DT) and diffusion orientation distribution functions (dODFs) from high-resolution T1w images. Our integration of the log-Euclidean Metric into a learning objective guarantees, unlike standard Euclidean networks, the mathematically-valid synthesis of diffusion. Furthermore, our approach improves the fractional anisotropy mean squared error (FA MSE) between the synthesized diffusion and the ground-truth by more than 23% and the cosine similarity between principal directions by almost 5% when compared to our baselines. We validate our generated diffusion by comparing the resulting tractograms to our expected real data. We observe similar fiber bundles with streamlines having <3% difference in length, <1% difference in volume, and a visually close shape. While our method is able to generate diffusion images from structural inputs in a high-resolution space within 15 s, we acknowledge and discuss the limits of diffusion inference solely relying on T1w images. Our results nonetheless suggest a relationship between the high-level geometry of the brain and its overall white matter architecture that remains to be explored. Frontiers Media S.A. 2022-09-08 /pmc/articles/PMC10406190/ /pubmed/37555146 http://dx.doi.org/10.3389/fnimg.2022.930496 Text en Copyright © 2022 Anctil-Robitaille, Théberge, Jodoin, Descoteaux, Desrosiers and Lombaert. 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 | Neuroimaging Anctil-Robitaille, Benoit Théberge, Antoine Jodoin, Pierre-Marc Descoteaux, Maxime Desrosiers, Christian Lombaert, Hervé Manifold-aware synthesis of high-resolution diffusion from structural imaging |
title | Manifold-aware synthesis of high-resolution diffusion from structural imaging |
title_full | Manifold-aware synthesis of high-resolution diffusion from structural imaging |
title_fullStr | Manifold-aware synthesis of high-resolution diffusion from structural imaging |
title_full_unstemmed | Manifold-aware synthesis of high-resolution diffusion from structural imaging |
title_short | Manifold-aware synthesis of high-resolution diffusion from structural imaging |
title_sort | manifold-aware synthesis of high-resolution diffusion from structural imaging |
topic | Neuroimaging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406190/ https://www.ncbi.nlm.nih.gov/pubmed/37555146 http://dx.doi.org/10.3389/fnimg.2022.930496 |
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