学术文献库

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