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Hierarchical microstructure informed tractography

Abstract : Background: Tractography uses diffusion magnetic resonance imaging to noninvasively infer the macroscopic pathways of white matter fibers and it is the only available technique to probe in vivo the structural connectivity of the brain. However, despite this unique and compelling ability and its wide range of possible neurological applications, tractography is still limited, lacks anatomical precision, and suffers from a serious sensitivity/specificity trade-off. For this reason, in the past few years, tractography postprocessing techniques have emerged and proved effective for improving the quality of the reconstructions. Among them, the Convex Optimization Modeling for Microstructure Informed Tractography formulation allows incorporating the anatomical prior that fibers are naturally organized in fascicles, and has obtained exceptional results in increasing the accuracy of the estimated tractograms. Methods: We propose an extension to this idea and introduce a multilevel grouping of the streamlines to capture the white matter arrangement in fascicles and subfascicles. We tested our proposed formulation in synthetic and in vivo data. Results: Our experiments show that using multiple levels allows considering information about the white matter organization more adequately and helps to improve further the accuracy of the resulting tractograms. Conclusion: This new formulation represents a further important step toward a more accurate structural connectivity estimation.
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https://hal.archives-ouvertes.fr/hal-03225523
Contributor : Laurent Petit Connect in order to contact the contributor
Submitted on : Wednesday, May 12, 2021 - 4:14:00 PM
Last modification on : Tuesday, May 25, 2021 - 11:44:01 AM
Long-term archiving on: : Friday, August 13, 2021 - 6:43:50 PM

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Mario Ocampo-Pineda, Simona Schiavi, François Rheault, Gabriel Girard, Laurent Petit, et al.. Hierarchical microstructure informed tractography. Brain connectivity, Mary Ann Liebert, Inc. publishers, 2021, 11 (2), pp.75-88. ⟨10.1089/brain.2020.0907⟩. ⟨hal-03225523⟩

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