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• W3111584674 endingPage "117617" @default.
• W3111584674 startingPage "117617" @default.
• W3111584674 abstract "At the typical spatial resolution of MRI in the human brain, approximately 60–90% of voxels contain multiple fiber populations. Quantifying microstructural properties of distinct fiber populations within a voxel is therefore challenging but necessary. While progress has been made for diffusion and T1-relaxation properties, how to resolve intra-voxel T2 heterogeneity remains an open question. Here a novel framework, named COMMIT-T2, is proposed that uses tractography-based spatial regularization with diffusion-relaxometry data to estimate multiple intra-axonal T2 values within a voxel. Unlike previously-proposed voxel-based T2 estimation methods, which (when applied in white matter) implicitly assume just one fiber bundle in the voxel or the same T2 for all bundles in the voxel, COMMIT-T2 can recover specific T2 values for each unique fiber population passing through the voxel. In this approach, the number of recovered unique T2 values is not determined by a number of model parameters set a priori, but rather by the number of tractography-reconstructed streamlines passing through the voxel. Proof-of-concept is provided in silico and in vivo, including a demonstration that distinct tract-specific T2 profiles can be recovered even in the three-way crossing of the corpus callosum, arcuate fasciculus, and corticospinal tract. We demonstrate the favourable performance of COMMIT-T2 compared to that of voxelwise approaches for mapping intra-axonal T2 exploiting diffusion, including a direction-averaged method and AMICO-T2, a new extension to the previously-proposed Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework." @default.
• W3111584674 created "2020-12-21" @default.
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• W3111584674 date "2021-02-01" @default.
• W3111584674 modified "2023-10-12" @default.
• W3111584674 title "Resolving bundle-specific intra-axonal T2 values within a voxel using diffusion-relaxation tract-based estimation" @default.
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• W3111584674 doi "https://doi.org/10.1016/j.neuroimage.2020.117617" @default.
• W3111584674 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33301934" @default.
• W3111584674 hasPublicationYear "2021" @default.
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