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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 FSE sequence.

FIGURE 1 (a)The overall architecture of the proposed cGAN-based method.(b) The discriminator framework, consists of 5 cascaded convolution layers.(c) The generator framework, it contains 5 encode and 5 decode block, and 7 cascaded Resblock.(d)The entropy curve, shows that the motion amplitude increase, the entropy increase.

FIGURE 3 Mild and strong motion correction results of various methods on multi-shot FSE sequence. Columns of the first is the mild motion result and the third is strong. The columns from left to right show reference images, motion-affected images, motion corrected images using TV Denoiser, DnCNN, UNET, and our method, respectively. In the second and fourth row, the absolute error maps corresponding to the first and third row are presented. In each motion corrected image, the correction quantitatively (PSNR, SSIM, MSE) can be seen in comparison to the motion-free reference.