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Home arrow Computer Science arrow Computational Diffusion MRI: MICCAI Workshop, Athens, Greece, October 2016

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Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framelets

Jian Zhang, Geng Chen, Yong Zhang, Bin Dong, Dinggang Shen, and Pew-Thian Yap

Abstract Noise in diffusion-weighted (DW) images increases the complexity of quantitative analysis and decreases the reliability of inferences. Hence, to improve analysis, it is often desirable to remove noise and at the same time preserve relevant image features. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of DW images. Our approach (1) employs the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders; (2) introduces a very efficient method for solving an '0 denoising problem that involves only thresholding and solving a trivial inverse problem; and (3) groups DW images acquired with neighboring gradient directions for collaborative denoising. Experiments using synthetic data with noncentral chi noise and real data with repeated scans confirm that our method yields superior performance compared with denoising using state-of-the-art methods such as non-local means.

J. Zhang

School of Information and Electrical Engineering, Hunan University of Science & Technology, Xiangtan, China

Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA e-mail: This email address is being protected from spam bots, you need Javascript enabled to view it

G. Chen

Data Processing Center, Northwestern Polytechnical University, Xi’an, China

Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA

Y. Zhang

Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA B. Dong

Beijing International Center for Mathematical Research, Peking University, Beijing, China D. Shen • P.-T. Yap (H)

Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA e-mail: This email address is being protected from spam bots, you need Javascript enabled to view it © Springer International Publishing AG 2017

A. Fuster et al. (eds.), Computational Diffusion MRI, Mathematics

and Visualization, DOI 10.1007/978-3-319-54130-3_4

 
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