Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver.

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Abstract

To improve the quality of images obtained via dynamic contrast enhanced MRI (DCE-MRI), which contain motion artifacts and blurring using a deep learning approach.
A multi-channel convolutional neural network-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, which contained breath-hold failures occurring 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.
The proposed network was found to be significantly reducing 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.
A deep learning-based method for removing motion artifacts in images obtained via DCE-MRI of the liver was demonstrated and validated.