Meeting Banner
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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here