Open AccessRetrospective motion correction for Fast Spin Echo based on conditional GAN with entropy loss
Author(s) -
Yalei Chen,
Ping-An Li,
Kewen Liu,
Qingjia Bao,
Li Zhao,
Xiaojun Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012118
Subject(s) - discriminator , entropy (arrow of time) , artificial intelligence , computer science , image quality , encoder , computer vision , conditional entropy , pattern recognition (psychology) , physics , image (mathematics) , principle of maximum entropy , telecommunications , quantum mechanics , detector , operating system
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.