DT-MRI regularization using 3D nonlinear gradient vector flow anisotropic diffusion.
In DT-MRI, diffusion-weighted multislice echoplanar images (EPIs) are processed to represent water diffusion characteristics as a diffusion tensor, reflecting the amount of diffusion in 3D. However imaging quality is generally compromised by several factors including the number of imaging slices, averages, diffusion sensitization steps (b-values), voxel size, and gradient directions, resulting in suboptimal SNR. In this study, we focus on improving imaging quality and SNR by denoising and reducing systematic and random errors through nonlinear anisotropic regularization. The raw EPIs are directly regularized through a newly proposed nonlinear anisotropic diffusion regularization method in 3D utilizing the gradient vector flow fields and its performance is compared to conventional 2D and vector-valued 2D anisotropic regularization methods. The effects of these variants of anisotropic regularization are examined through the maps of color-coded fractional anisotropy and tracked neural fibers. The results show that DT-MRI regularization using the proposed 3D anisotropic diffusion significantly improves the quality of fiber tracking and diffusion indices such as fractional anisotropy.
Kim
T S
TS
Department of Biomedical Engineering, Kyung Hee University, Kyungki, South Korea.
Kim
S
S
Hwang
D
D
Singh
M
M