Abstract #2813
Denoising Diffusion-Weighted Images by Using Higher-Order Singular Value Decomposition
Xinyuan Zhang 1 , Man Xu 1 , Zhe Zhang 2 , Hua Guo 2 , Fan Lam 3 , Zhipei Liang 3 , Qianjin Feng 1 , Wufan Chen 1 , and Yanqiu Feng 1
1
Biomedical Engineering, Guangdong Provincial
Key Laborary of Medical Image Processing, Southern
Medical University, Guangzhou, Guangdong, China,
2
Biomedical
Engineering, Center for Biomedical Imaging
Research,Tsinghua University, Beijing, Beijing, China,
3
Electrical
and Computer Engineering, University of Illinois at
Urbana-Champaign, Urbana, Illinois, United States
Diffusion-weighted (DW) magnetic resonance imaging is
widely used in clinic and research because of its
ability to characterize the diffusion of water molecules
within tissue. However, the DW images are usually
affected by severe noise especially at high resolution
and high b values, and the low signal-to-noise ratio may
degrade the reliability of the subsequent quantitative
analysis. Recently, a patch-based higher-order singular
value decomposition (HOSVD) method was proposed to
denoise MR images and demonstrated to outperform the
well-known BM4D method. Compared with the conventional
T1-, T2- and proton density (PD)-weighted images, DW
images may contain more redundant information because
that they are usually highly correlated across different
diffusion directions. In this work, we proposed to
simultaneously exploit the redundant information along
diffusion directions and across spatial domain by using
HOSVD in denoising DW images.
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