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bioRxiv · 10.1101/2021.04.23.441167

Image-based Motion Artifact Reduction on Liver Dynamic Contrast Enhanced MRI

Wu, Y.·Liu, J.·White, G. M.·Deng, J.
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Abstract

Liver MRI images often suffer degraded quality from ghosting or blurring artifact caused by patient respiratory or bulk motion. In this study, we developed a two-stage deep learning model to reduce motion artifact on dynamic contrast enhanced (DCE) liver MRIs. The stage-I network utilized a deep residual network with a densely connected multi-resolution block (DRN-DCMB) network to remove the majority of motion artifacts. The stage-II network applied the perceptual loss to preserve image structural features by updating the parameters of the stage-I network via backpropagation. The stage-I network was trained using small image patches simulated with five types of motion, i.e., rotational, sinusoidal, random, elastic deformation and through-plane, to mimic actual liver motion patterns. The stage-II network training used full-size images with the same types of motion as the stage-I network. The motion reduction deep learning model was testing using simulated motion images and images with real motion artifacts. The resulted images after two-stage processing demonstrated substantially reduced motion artifacts while preserved anatomic details without image blurriness. This model outperformed existing methods of motion reduction artifact on liver DCE-MRI.

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Wu, Y., Liu, J., White, G. M., Deng, J.. 2021-04-24. Image-based Motion Artifact Reduction on Liver Dynamic Contrast Enhanced MRI. https://doi.org/10.1101/2021.04.23.441167

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