# Method for motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MRI of the liver

arXiv: https://arxiv.org/abs/1807.06956 · HTML: https://mlsanity.com/paper/1807.06956/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2018.

## Built on by (most cited first, 7 of 7)

- [Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review](https://mlsanity.com/paper/2305.06739/) (2023)
- [Learning MRI Artifact Removal With Unpaired Data](https://mlsanity.com/paper/2110.04604/) (2021)
- [Annealed Score-Based Diffusion Model for MR Motion Artifact Reduction](https://mlsanity.com/paper/2301.03027/) (2023)
- [Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning](https://mlsanity.com/paper/2105.00240/) (2021)
- [Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.05569/) (2022)
- [Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction](https://mlsanity.com/paper/2509.05071/) (2025)
- [Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging](https://mlsanity.com/paper/2301.01732/) (2023)

## Abstract (arXiv)

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.