Toward nonparametric diffusion-T1 characterization of crossing fibers in the human brain

Abstract

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