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Abstract #1353

Retrospective motion correction for Fast Spin Echo  based on conditional GAN with entropy loss

Qingjia Bao1, Yalei Chen2, Pingan Li2, Kewen Liu2, Zhao Li3, Xiaojun Li2, Fang Chen3, and Chaoyang Liu3
1Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel, 2School of Information Engineering, Wuhan University of Technology, Wuhan, China, 3State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences., Wuhan, China

We proposed a new end-to-end motion correction method based on conditional generative adversarial network (GAN) and minimum entropy of MRI images for Fast Spin Echo(FSE) sequence. The network contains an encoder-decoder generator to generate the motion-corrected images and a PatchGAN discriminator to classify an image as either real (motion-free) or fake(motion-corrected). Moreover, the image's entropy is set as one loss item in the cGAN's loss as the entropy increases monotonically with the motion amplitude, indicating that entropy is a good criterion for motion. The results show that the proposed method can effectively reduce the artifacts and obtain high-quality motion-corrected images from the motion-affected images in both pre-clinical and clinical datasets.

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