{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T16:48:30Z","timestamp":1792687710435,"version":"4.5.0"},"reference-count":87,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:p>Traditional Diffusion Tensor Imaging (DTI) metrics are affected by crossing fibers and lesions. Most of the previous tractometry works use the single diffusion tensor, which leads to limited sensitivity and challenging interpretation of the results in crossing fiber regions. In this work, we propose a tractometry pipeline that combines white matter tractography with multi-tensor fixel-based metrics. These multi-tensors are estimated using the stable, accurate and robust to noise Multi-Resolution Discrete Search method (MRDS). The spatial coherence of the multi-tensor field estimated with MRDS, which includes up to three anisotropic and one isotropic tensors, is tractography-regularized using the Track Orientation Density Imaging method. Our end-to-end tractometry pipeline goes from raw data to track-specific multi-tensor-metrics tract profiles that are robust to noise and crossing fibers. A comprehensive evaluation conducted in a phantom simulating healthy and damaged tissue with the standard model, as well as in a healthy cohort of 20 individuals scanned along 5 time points, demonstrates the advantages of using multi-tensor metrics over traditional single-tensor metrics in tractometry. Qualitative assessment in a cohort of patients with relapsing-remitting multiple sclerosis reveals that the pipeline effectively detects white matter anomalies in the presence of crossing fibers and lesions.<\/jats:p>","DOI":"10.3389\/fnins.2024.1467786","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T15:03:50Z","timestamp":1734707030000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-tensor fixel-based metrics in tractometry: application to multiple sclerosis"],"prefix":"10.3389","volume":"18","author":[{"given":"Erick","family":"Hernandez-Gutierrez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ricardo","family":"Coronado-Leija","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manon","family":"Edde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthieu","family":"Dumont","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Christophe","family":"Houde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhamed","family":"Barakovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Magon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alonso","family":"Ramirez-Manzanares","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxime","family":"Descoteaux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"3841","DOI":"10.1002\/nbm.3841","article-title":"Imaging brain microstructure with diffusion MRI: practicality and applications","volume":"32","author":"Alexander","year":"2017","journal-title":"NMR Biomed"},{"key":"B2","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1016\/S1053-8119(03)00336-7","article-title":"How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging","volume":"20","author":"Andersson","year":"2003","journal-title":"Neuroimage"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1016\/j.neuroimage.2015.10.019","article-title":"An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging","volume":"125","author":"Andersson","year":"2016","journal-title":"Neuroimage"},{"key":"B4","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1006\/jmrb.1994.1037","article-title":"Estimation of the effective self-diffusion tensor from the nmr spin echo","volume":"103","author":"Basser","year":"1994","journal-title":"J. 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