Toward nonparametric diffusion‐ [Formula: see text] characterization of crossing fibers in the human brain

PURPOSE: To estimate [Formula: see text] for each distinct fiber population within voxels containing multiple brain tissue types. METHODS: A diffusion‐ [Formula: see text] 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 [Formula: see text] of diffusion tensors and longitudinal relaxation rates [Formula: see text]. Orientation distribution functions (ODFs) of the highly anisotropic components of [Formula: see text] 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 [Formula: see text] ’s statistical descriptors were obtained, exhibiting the expected [Formula: see text] contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in [Formula: see text] 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‐ [Formula: see text] features, thereby showing potential for characterizing developmental or pathological changes in [Formula: see text] within a given fiber bundle, and for investigating interbundle [Formula: see text] differences.