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

Abstract

Purpose: To estimate $T_1$ for each distinct fiber population within voxels containing multiple brain tissue types.

Methods: A diffusion- $T_1$ 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 $P(D,R_1)$ of diffusion tensors and longitudinal relaxation rates $R_1=1/T_1$ . Orientation distribution functions (ODFs) of the highly anisotropic components of $P(D,R_1)$ 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 $P(D,R_1)$ 's statistical descriptors were obtained, exhibiting the expected $R_1$ contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in $R_1$ 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- $T_1$ features, thereby showing potential for characterizing developmental or pathological changes in $T_1$ within a given fiber bundle, and for investigating interbundle $T_1$ differences.

Keywords: diffusion-relaxation correlation; fiber-specific microstructure; inverse Laplace transform; multivariate distribution; orientation distribution function; tensor-valued diffusion encoding.