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