Motion artifact is a problem in abdominal imaging. Respiratory-triggering techniques are commonly used to suppress the motion artifact. However, it is not always perfect in clinical practice. Deep learning-based motion correction is an attractive solution. However, it requires pairs of images with and without motion artifacts, which is difficult in body MRI. Here, we propose a deep learning-based method to remove motion artifact using a simulation of artifacts based on a simple model for respiratory gating failure. Preliminary results showed that the proposed method can remove motion artifacts in respiratory-triggered FSE-T2WI of the liver, which were corrupted by irregular breathing.