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Abstract #4774

DCTV-Net: Model basedĀ Convolutional Neural Network for dynamic MRI

Shanshan Wang1, Yanxia Chen1, Leslie Ying2, Cheng Li1, Ziwen Ke1, Taohui Xiao1, Xin Liu1, Dong Liang1, and Hairong Zheng1

1Shenzhen Institutes of Advanced Technologies, Xili Nanshan, China, 2Department of Biomedical Engineering and Department of Electrical Engineering, The State University of New York, Buffalo, NY, United States

Compressive sensing MRI (CS-MRI) is a popular technique to accelerate MR dynamic imaging. Nevertheless, the reconstruction is normally time-consuming and its parameters have to be hand-tuned To address this challenge, we solve a CS-based dynamic MR imaging problem by adopting the Alternating Direction Method of Multipliers (ADMM) iteration method with the most popular deep learning technique. Specifically, we introduce a deep network structure, dubbed as DCTV-NET, for dynamic magnetic resonance image reconstruction from highly under-sampled k-t space data. Experimental results demonstrate that our method is superior to the state-of-the-art dynamic MRI methods.

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