Retrospective motion correction for Fast Spin Echo based on conditional GAN with entropy loss
Autor: | Zhao Li, Kewen Liu, Yalei Chen, Pingan Li, Xiaojun Li, Qingjia Bao |
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Rok vydání: | 2021 |
Předmět: | |
Zdroj: | Journal of Physics: Conference Series. 1848:012118 |
ISSN: | 1742-6596 1742-6588 |
Popis: | 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. |
Databáze: | OpenAIRE |
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