Unsupervised cycle-consistent network for removing susceptibility artifacts in single-shot EPI
Weida Xie (Wuhan University of Technology)*; 适 陈 (Wuhan University of Technology); Qingjia Bao (Chinese Academy of Sciences); 可文 刘 (Wuhan University of Technology); Zhao Li (State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Math, Innovation Academy for Precision Measurement Science and Technology. ); Chongxin Bai (Wuhan University of Technology); Otikovs Martins (Weizmann Institute of Science, Rehovot, 76001); Piqiang Li (Wuhan University of Technology); Jie Wang (Chinese Academy of Sciences)PMLR Page
Single-shot EPI(ssEPI) is one of the most important ultrafast MRI sequences commonly used for diffusion-weighted MRI and functional MRI. However, ssEPI suffers from susceptibility artifacts, especially in the high field or at the tissue boundaries. The widely used blip/down approaches, such as TOPUP, estimate the underlying distortion field from a pair of images with reversed-phase encoding direction. Typically, the iterative methods are used to find a solution to the ill-posed problem of finding the displacement map that maps up/down acquisitions onto each other. Then the geometric and intensity corrections are applied to obtain the undistorted images based on the estimated displacement map. This paper presents a new unsupervised cycle-consistent deep neural network that takes advantage of both the deep neural network and the gradient reversal method. The proposed method consists of three main components: (1) the Resnet50-Unet to map the pair of images with inverted phase encoding to the displacement maps; (2) the geometric and intensity correction module to obtain the undistorted images; (3) the forward model is applied to get the cycled blip up/down images, and the cycle-consistent loss is optimized. In addition, the CNN network will generate two field maps to overcome motion or field drift during the scan. This new network is trained unsupervised on the clinical datasets downloaded from the Human Connection Project website. And we test this method on both preclinical and clinical datasets. The preclinical dataset is collected from 20 mice based on the modified EPI pulse sequence in 7T scanner. Both simulated and experimental results demonstrate that our method outperforms state-of-the-art methods. In conclusion, we proposed an unsupervised cycle-consistent deep neural network for removing susceptibility artifacts. The results on both preclinical and clinical datasets show this new method's acceleration and generalization capabilities.