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Abstract #1000

An Unsupervised Deep Learning Method for Parallel MR Cardiac Imaging via Time Interleaved Sampling

Ziwen Ke1,2, Yanjie Zhu3, Jing Cheng2,3, Leslie Ying4, Xin Liu3, Hairong Zheng3, and Dong Liang1,3
1Research Center for Medical AI, Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences, Shenzhen, China, 2Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China, 3Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences, Shenzhen, China, 4Department of Biomedical Engineering and Department of Electrical Engineering, The State University of New York, Buffalo, NY, United States

Deep learning has achieved good success in cardiac MRI. However, these methods are all based on big data, and only deal with single-channel imaging. In this paper, we propose an unsupervised deep learning method for parallel MR cardiac imaging via time interleaved sampling. Specifically, a set of full-encoded reference data were built by merging the data from adjacent time frames, and used to train a network for reconstructing each coil image separately. Finally, coil images were combined via another CNN. The validation on in vivo data show that our method can achieve improved reconstruction compared with other competing methods.

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