Simulation and Mitigation of the Wrap-Around Artifact in the MRI Image
Runze Hu1, Rui Yang1, Yutao Liu2
1Department of Information Science and Technology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Abstract:
Magnetic resonance imaging (MRI) is an essential clinical imaging modality for diagnosis and medical research, while various artifacts occur during the acquisition of MRI image, resulting in severe degradation of the perceptual quality and diagnostic efficacy. To tackle such challenges, this study deals with one of the most frequent artifact sources, namely the wrap-around artifact. In particular, given that the MRI data are limited and difficult to access, we first propose a method to simulate the wrap-around artifact on the artifact-free MRI image to increase the quantity of MRI data. Then, an image restoration technique, based on the deep neural networks, is proposed for wrap-around artifact reduction and overall perceptual quality improvement. This study presents a comprehensive analysis regarding both the occurrence of and reduction in the wrap-around artifact, with the aim of facilitating the detection and mitigation of MRI artifacts in clinical situations.
Insights
This study introduces a method to simulate magnetic resonance imaging (MRI) wrap-around artifacts and uses deep neural networks to reduce them, improving image quality for better diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Image Processing
Background:
- Magnetic Resonance Imaging (MRI) is crucial for clinical diagnosis and research.
- MRI artifacts, such as wrap-around, degrade image quality and diagnostic accuracy.
- Limited availability of MRI data hinders artifact research and development.
Purpose of the Study:
- To address the challenge of limited MRI data by simulating wrap-around artifacts.
- To develop and evaluate a deep neural network-based method for MRI wrap-around artifact reduction.
- To improve the perceptual quality and diagnostic efficacy of MRI images affected by artifacts.
Main Methods:
- Simulated wrap-around artifacts on artifact-free MRI images to augment dataset.
- Developed a deep neural network for image restoration and artifact reduction.
- Comprehensive analysis of artifact occurrence and mitigation strategies.
Main Results:
- Successfully simulated realistic wrap-around artifacts, increasing MRI data quantity.
- Demonstrated significant reduction in wrap-around artifacts using the proposed deep learning model.
- Achieved overall improvement in MRI image perceptual quality.
Conclusions:
- The proposed artifact simulation method effectively expands MRI datasets.
- Deep neural networks offer a promising approach for mitigating MRI wrap-around artifacts.
- This work facilitates the detection and reduction of MRI artifacts in clinical practice.
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