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.