Abstract #2503
Sparsity-Promoting Orthogonal Dictionary Updating for Highly Undersampled MRI Reconstruction
Jinhong Huang 1,2 , Xiaohui Liu 1 , Wufan Chen 1 , and Yanqiu Feng 1
1
Guangdong Provincial Key Laborary of Medical
Image Processing, School of Biomedical Engineering,
Southern Medical University, Guangzhou, Guangdong,
China,
2
School
of Mathemtics and Computer Science, Gannan Normal
University, Ganzhou, Jiangxi, China
Image reconstruction employing adaptive sparsifying
transform has demonstrated promising performance in
compressed sensing magnetic resonance imaging. However,
conventional overcomplete dictionary learning based
methods are computationally expensive. In this work, we
present a novel sparsity-promoting orthogonal dictionary
updating method (SPODU) for efficient image
reconstruction from highly undersampled MRI data. To
further improve reconstruction, the deterministic
annealing like strategy is combined into the algorithm.
Experimental results demonstrate that the proposed SPODU
algorithm is more efficient and accurate than the
dictionary learning based method which using K-SVD for
sparse coding, and thus has potential application in
practice.
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