Motion artifact correction of MRI based on a generative adversarial network and domain consistency
宪华 曾, 聪辉 纪, 倩 董 · Scientia Sinica Informationis · 2021
In clinical diagnosis, magnetic resonance imaging (MRI) motion artifact is a common problem that will affect the doctor's diagnosis. Although the reacquisition of MRI can avoid this problem, it will bring extra economic and time cost to hospitals and patients. Therefore, the correction of motion artifacts has a practical research value. Existing studies mainly focus on correcting motion artifacts from the spatial domain or K-space, and ignore the data consistency between them. In order to solve this problem, a motion artifact correction model based on a generative adversarial network is proposed to maintain the data consistency between the K-space and spatial domain. In this model, K-space data are initially corrected by a frequency domain generator, and then spatial domain data are fine corrected by a spatial domain generator. In optimization phase, the data consistency loss between the K-space and spatial domain is used to maintain data consistency. In four public MRI datasets, ADNI, ABIDE, OASIS and Brain, the experimental results show that the performance of the proposed model has increased by 3.4%, 3.07% and 15.57% on PSNR, SSIM and RMSE, respectively.