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Abstract #4334

k-Space Interpolation for Accelerated MRI Using Deep Generative Models

Zhuo-Xu Cui1, Sen Jia2, Zhilang Qiu2, Qingyong Zhu1, Yuanyuan Liu3, Jing Cheng2, Leslie Ying4, Yanjie Zhu2, and Dong Liang1
1Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 3National Innovation Center for Advanced Medical Devices, Shenzhen, China, 4Department of Biomedical Engineering and the Department of Electrical Engineering, The State University of New York, Buffalo, NY, United States

Synopsis

k-space deep learning (DL) is emerging as an alternative to the conventional image domain DL for accelerated MRI. Typically, DL requires training on large amounts of data, which is unaccessible in clinical. This paper proposes to present an untrained k-space deep generative model (DGM) to interpolate missing data. Specifically, missing data is interpolated by a carefully designed untrained generator, of which the output layer conforms the MR image multichannel prior, while the architecture of other layers implicitly captures k-space statistics priors. Furthermore, we prove that the proposed method guarantees enough accuracy bounds for interpolated data under commonly used sampling patterns.

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