Resolving bundle-specific intra-axonal T(2) values within a voxel using diffusion-relaxation tract-based estimation.
Resolving bundle-specific intra-axonal T(2) values within a voxel using diffusion-relaxation tract-based estimation.
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DOI:
10.1016/j.neuroimage.2020.117617
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发表时间:
2021-02-15
期刊:
影响因子:
5.7
通讯作者:
Jones DK
中科院分区:
文献类型:
--
作者:
Barakovic M;Tax CMW;Rudrapatna U;Chamberland M;Rafael-Patino J;Granziera C;Thiran JP;Daducci A;Canales-Rodríguez EJ;Jones DK
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.
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影响因子:
5.7
作者:
Daducci, Alessandro;Canales-Rodriguez, Erick J.;Thiran, Jean-Philippe
通讯作者:
Thiran, Jean-Philippe
DOI:
10.1006/jmrb.1994.1037
发表时间:
1994-03-01
期刊:
JOURNAL OF MAGNETIC RESONANCE SERIES B
影响因子:
--
作者:
BASSER, PJ;MATTIELLO, J;LEBIHAN, D
通讯作者:
LEBIHAN, D
影响因子:
0.6
作者:
Edén, M
通讯作者:
Edén, M
影响因子:
5.7
作者:
Dale, AM;Fischl, B;Sereno, MI
通讯作者:
Sereno, MI
影响因子:
4.6
作者:
Hutter J;Slator PJ;Christiaens D;Teixeira RPAG;Roberts T;Jackson L;Price AN;Malik S;Hajnal JV
通讯作者:
Hajnal JV