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Abstract #1423

Multi-contrast image guided graph representation and its application in compressed sensing MRI reconstruction

Zongying Lai1, Xiaobo Qu2, Jiaxi Ying2, Hengfa Lu2, Zhifang Zhan2, Di Guo3, and Zhong Chen2

1Dept. of Communication Engineering, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China, xiamen, People's Republic of China, 2Dept. of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China, People's Republic of China, 3Dept. of Computer Science, Xiamen University of Technology, Xiamen 361024, China, People's Republic of China

Under-sampling the k-space data and reconstructing images with sparsity constraint is one efficient way to accelerate magnetic resonance imaging However, achieving high acceleration factor is challenging since image structures may be lost or blurred when the sampled information is not sufficient. In this work, we propose a new approach to reconstruct magnetic resonance images by learning the prior knowledge from multi-contrast images with graph-based sparsifying transform. To incorporate extra information from multi-contrast image, registration is included in a bi-level optimization frame as well as the sparse reconstruction. Experiment results demonstrate that the proposed method outperforms the state-of-art with high accelerating factor.

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