Yujiao Zhao1,2, Linfang Xiao1,2, Zhe Zhang3, Yilong Liu1,2, Hua Guo4, and Ed X. Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China, 3China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, 4Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China, 3China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, 4Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
A
joint denoising method for diffusion-weighted images using low-rank matrix
approximation is proposed. It exploits structural similarities of DW images, leading
to significant noise reduction in all DW images and revealing more microstructural
details in quantitative diffusion maps.
Fig. 1. Diagram of the proposed joint denoising method.
Within each iteration: (1) extracting reference patches using a sliding window
and searching for similar patches through block matching; (2) for each
reference patch, stretching
its similar patches to vectors and stacking them into a matrix to form a
low-rank patch matrix; (3) for
each patch matrix, estimating a noise-free patch matrix through a
weighted nuclear norm minimization (WNNM) model; (4) converting estimated patch
matrices back to images.
Fig. 3. Denoising results with in vivo DW brain images (A) and resulting
diffusion metric maps computed from denoised DW images (B). The image set contains one b0 image and 6 DW images with b =2000
s/mm2. Only DW image along one
direction is shown. The image set of NEX=1 was used for denoising, while
the image set of NEX=4/10 was used as high SNR reference. At very low SNR, the proposed method
was still robust and more effective than MPPCA in reducing noise while
recovering structural details when compared to reference. It achieved image
quality and FA map comparable to those using 4 averages.