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Abstract #3495

Weakly Supervised Deep Prior Learning for Multi-coil MRI Reconstruction

Haoyun Liang1, Taohui Xiao1, Chuyu Rong1, Yu Gong1, Cheng Li1, and Shanshan Wang1
1Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, Chinese Academy of Sciences, Shenzhen, P.R.China, Shenzhen, China

MRI reconstruction based on supervised learning methods, have achieved remarkable success. However, because of the difficulty and high cost of the MR images collection process, it is not always easy to obtain a big dataset with strong supervised information. Therefore, weakly supervised learning will be a possible solution. In this work, we proposed a weakly deep prior learning algorithm to train a complex UNet for multi-coil MR images reconstruction without very precise labels. The result shows that our proposed algorithm can provide a competitive performance compared to the classical methods with enoucraging quantitative indicators of SSIM and PSNR.

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