Improving the image quality of liver DWI using the convolutional neural network-based selection algorithm

Diffusion-weighted imaging (DWI) of the liver using a single-shot EPI sequence suffer from motion artifact caused by cardiac motion. The reconstruction of DWI with multiple numbers of excitation including the corrupted echoes due to systolic cardiac motion results in a severe signal loss in the left lobes, even if other echoes in diastolic phase had no artifact. In this study, we propose a selection algorithm to reject the corrupted echoes using convolutional neural network was proposed. The volunteer studies demonstrated that the proposed method improves the image quality of liver DWI.