  Purpose: To improve the quality of images obtained via dynamic
contrast-enhanced MRI (DCE-MRI) that include motion artifacts and blurring
using a deep learning approach. Methods: A multi-channel convolutional neural
network (MARC) based method is proposed for reducing the motion artifacts and
blurring caused by respiratory motion in images obtained via DCE-MRI of the
liver. The training datasets for the neural network included images with and
without respiration-induced motion artifacts or blurring, and the distortions
were generated by simulating the phase error in k-space. Patient studies were
conducted using a multi-phase T1-weighted spoiled gradient echo sequence for
the liver containing breath-hold failures during data acquisition. The trained
network was applied to the acquired images to analyze the filtering
performance, and the intensities and contrast ratios before and after denoising
were compared via Bland-Altman plots. Results: The proposed network was found
to significantly reduce the magnitude of the artifacts and blurring induced by
respiratory motion, and the contrast ratios of the images after processing via
the network were consistent with those of the unprocessed images. Conclusion: A
deep learning based method for removing motion artifacts in images obtained via
DCE-MRI in the liver was demonstrated and validated.
