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Abstract #0410

A Fast Algorithm for Rank and Edge Constrained Denoising of Magnitude Diffusion-Weighted Images

Fan Lam 1,2 , Ding Liu 1,2 , Zhuang Song 3,4 , Michael W Weiner 3,4 , Norbert Schuff 3,4 , and Zhi-Pei Liang 1,2

1 Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States, 2 Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States, 3 Department of Veteran Affairs Medical Center, San Francisco, California, United States, 4 Radiology and Biomedical Imaging, University of California, San Francisco, California, United States

We developed a new fast algorithm for joint rank and edge constrained denoising of 3D magnitude diffusion-weighted image sequences by extending a recently proposed majorize-minimize framework for statistical estimation with noncentral χ distributions. Specifically, we extended the framework to consider joint rank and edge constraints, deriving a new algorithm that decomposes the original noncentral χ denoising problem into a series of rank and edge constrained Gaussian denoising problems. We show that the proposed algorithm achieves similar or even better denoising performance compared to a previously proposed algorithm, both qualitatively and quantitatively, but in significantly less time.

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