Denoising of brain DW-MR data by single and multiple diffusion kernels

Abstract

Diffusion Weighted Magnetic Resonance Imaging is widely used to study the structure of the fiber pathways of white matter in the brain. However, the recovered axon orientations can be prone to error because of the low signal to noise ratio. Spatial regularization can reduce the error, but it must be done carefully so that real spatial information is not removed and false orientations are not introduced. In this paper we investigate the advantages of applying an anisotropic filter based on single and multiple axon bundle orientation kernels. To this end, we compute local diffusion kernels based on Diffusion Tensor and multi Diffusion Tensor models. We show the benefits of our approach to three different types of DW-MRI data: synthetic, in vivo human, and acquired from a diffusion phantom.