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Abdominal magnetic resonance imaging (MRI) provides a straightforward way ofcharacterizing tissue and locating lesions of patients as in standarddiagnosis. However, abdominal MRI often suffers from respiratory motionartifacts, which leads to blurring and ghosting that significantly deterioratethe imaging quality. Conventional methods to reduce or eliminate these motionartifacts include breath holding, patient sedation, respiratory gating, andimage post-processing, but these strategies inevitably involve extra scanningtime and patient discomfort. In this paper, we propose a noveldeep-learning-based model to recover MR images from respiratory motionartifacts. The proposed model comprises a densely connected U-net withgenerative adversarial network (GAN)-guided training and a perceptual lossfunction. We validate the model using a diverse collection of MRI data that areadversely affected by both synthetic and authentic respiration artifacts.Effective outcomes of motion removal are demonstrated. Our experimental resultsshow the great potential of utilizing deep-learning-based methods inrespiratory motion correction for abdominal MRI.

Respiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training