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Abstract #3630

Assessment of the Generalization of Learned Unsupervised Deep Learning Method

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

In our previous work, we proposed an unsupervised deep learning method for parallel MR cardiac imaging via time interleaved sampling. The comparisons with classical methods on in vivo data have shown that this method can achieve improved reconstruction results. However, the proposed unsupervised framework is based on the time interleaved sampling scheme. Does the model trained with time interleaved undersampling pattern have good generalization to other sampling patterns? In this paper, we will explore the generalization performance of the learned unsupervised deep learning method under different sampling patterns.

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