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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