Toward nonparametric diffusion- characterization of crossing fibers in the human brain.

Purpose: To estimate for each distinct fiber population within voxels containing multiple brain tissue types.

Methods: A diffusion- correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions of diffusion tensors and longitudinal relaxation rates . Orientation distribution functions (ODFs) of the highly anisotropic components of were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles.

Results: Parameter maps corresponding to 's statistical descriptors were obtained, exhibiting the expected contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup.

Conclusions: Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- features, thereby showing potential for characterizing developmental or pathological changes in within a given fiber bundle, and for investigating interbundle differences.