Resolving bundle-specific intra-axonal T2 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 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.