Diffusion Tensor Imaging Denoising Based on Riemannian Geometric Framework and Sparse Bayesian Learning

Abstract

Diffusion tensor imaging (DTI) is a special type of magnetic resonance imaging (MRI), which is the only noninvasive method that can effectively observe and trace the white matter fiber bundles of the brain. In the imaging process, the signal-to-noise ratio (SNR) of the MR image is low due to the influence of Rician noise. And it leads to processing difficultly by existing algorithms, which limits the development of DTI in clinical applications. In order to remove the Rician noise and preserve the diffusion tensor geometry of DTI, we propose a DTI denoising algorithm based on Riemannian geometric framework and sparse Bayesian learning. Firstly, DTI tensor is mapped to the Riemannian manifold to preserve the structural properties of the tensor. And then, sparse Bayesian learning method is used to reconstruct the noise-free DTI. The experimental results for synthetic or real DTI data show that the proposed algorithm effectively removes the Rician noise in the DTI as well as preserves the nonlinear structure of the DTI. Comparing with the existing denoising algorithm, the proposed algorithm has better denoising performance.