Resolving bundle-specific intra-axonal T values within a voxel using diffusion-relaxation tract-based estimation.

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 T-relaxation properties, how to resolve intra-voxel T heterogeneity remains an open question. Here a novel framework, named COMMIT-T, is proposed that uses tractography-based spatial regularization with diffusion-relaxometry data to estimate multiple intra-axonal T values within a voxel. Unlike previously-proposed voxel-based T estimation methods, which (when applied in white matter) implicitly assume just one fiber bundle in the voxel or the same T for all bundles in the voxel, COMMIT-T can recover specific T values for each unique fiber population passing through the voxel. In this approach, the number of recovered unique T 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 T 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-T compared to that of voxelwise approaches for mapping intra-axonal T exploiting diffusion, including a direction-averaged method and AMICO-T, a new extension to the previously-proposed Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework.