Magic DIAMOND: Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding

Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared conventional anatomical but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides voxel content into diffusion compartments and draws from diffusion-weighted data estimate compartmental non-central matrix-variate Gamma distribution of tensors, thereby resolving crossing fascicles while accounting for their respective heterogeneity. Alternatively, tensor-valued encoding defines new acquisition schemes tagging specific features intra-voxel directly outcome measurement. However, impact such on estimating brain has only been studied in a handful parametric single-fascicle models. In work, we derive general Laplace transform distribution, which enables extension encoded data. We then evaluate "Magic DIAMOND" silico vivo various combinations Assessing uncertainty parameter estimation via stratified bootstrap, investigate both voxel-based fixel-based metrics by carrying out multi-peak tractography. show that our estimated can be mapped along tracks robustly across regions fiber crossing, opens perspectives tractometry microstructure mapping white-matter tracts.