Segmentation of the brain using direction-averaged signal of DWI images

Segmentation of brain tissue in diffusion MRI image space has some unique advantages. A novel segmentation method using the direction-averaged diffusion weighted imaging (DWI) signal is proposed. Two images can be obtained from the fitting of the direction-averaged DWI signal as a function of b-value: one with superior contrast between the gray matter and white matter; one with prominent CSF contrast. A pseudo T1 weighted image can be constructed and standard segmentation tools can be applied. The method was tested on the HCP dataset using SPM12, and showed good agreement with segmentation using the T1 weighted image with the same resolution. The Dice score was all greater than 0.88 for GM or WM with full DWI data and very stable against subsampling of the DWI data in number of diffusion directions, number of shells, and spatial resolution

Comparison of different tensor encoding combinations in microstructural parameter estimation

Diffusion-weighted magnetic resonance imaging is a noninvasive tool to investigate the brain white matter microstructure. It provides the information to estimate the compartmental diffusion parameters. Several studies in the literature have shown that there is degeneracy in the estimated parameters using traditional linear diffusion encoding (Stejskal-Tanner pulsed gradient spin echo). Multiple strategies have been proposed to solve degeneracy, however, it is not clear if those methods would completely solve the problem. One of the approaches is b-tensor encoding. The combination of linear-spherical tensor encoding (LTE+STE) and linear-planar (LTE+PTE) have been utilized to make the estimations stable in the previous works. In this paper, we compare the results of fitting a two-compartment model using different combinations of b-tensor encoding. The four different combinations linear-spherical (LTE+STE), linear-planar (LTE+PTE), planar-spherical (PTE+STE) and linear-planar-spherical (LTE+PTE+STE) have been compared. The results show that the combination of tensor encodings leads to lower bias and higher precision in the parameter estimates than single tensor encoding