Abstract #3418
Novel Non-Local Total Variation Regularization for Constrained MR Reconstruction
Andres Saucedo 1,2 , Stamatios Lefkimmiatis 3 , Stanley Osher 3 , and Kyunghyun Sung 1,2
1
Department of Radiological Sciences, David
Geffen School of Medicine, University of California Los
Angeles, Los Angeles, California, United States,
2
Biomedical
Physics Interdepartmental Graduate Program, University
of California Los Angeles, Los Angeles, California,
United States,
3
Department
of Mathematics, University of California Los Angeles,
Los Angeles, California, United States
This study introduces a novel constrained reconstruction
technique that exploits both the local correlation of
image data across multiple coils and the inherent
non-local self-similarity property of images. Our
approach is based within a non-local total variation
regularization framework. The proposed method is
applicable to both compressed sensing and parallel
imaging, and demonstrates substantial advantages with
regard to high levels of noise.
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