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
Rok vydání:	2021
Předmět:	Physics History Entropy (classical thermodynamics) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Statistical physics Motion correction Fast spin echo Computer Science Applications Education
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
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::33bd541df929c918a80e70dd6ecaf586 https://doi.org/10.1088/1742-6596/1848/1/012118 Zobrazit plný text záznamu