title: Non-local mean denoising in diffusion tensor space.
abstract: The aim of the present study was to present a novel non-local mean (NLM) method to denoise diffusion tensor imaging (DTI) data in the tensor space. Compared with the original NLM method, which uses intensity similarity to weigh the voxel, the proposed method weighs the voxel using tensor similarity measures in the diffusion tensor space. Euclidean distance with rotational invariance, and Riemannian distance and Log-Euclidean distance with affine invariance were implemented to compare the geometric and orientation features of the diffusion tensor comprehensively. The accuracy and efficacy of the proposed novel NLM method using these three similarity measures in DTI space, along with unbiased novel NLM in diffusion-weighted image space, were compared quantitatively and qualitatively in the present study.
authors.affiliation: Department of Nephrology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China.
authors.forename: Baihai
authors.initials: B
authors.lastname: Su
authors.affiliation: Department of Nephrology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Liu
authors.affiliation: Department of Nephrology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China.
authors.forename: Jie
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Nephrology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China ; Department of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan 610225, P.R. China.
authors.forename: Xi
authors.initials: X
authors.lastname: Wu
keywords: denoising; diffusion tensor imaging; non-local mean