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A cardiac MRI motion artifact reduction method based on edge enhancement network
Nanhe Jiang1, Yucun Zhang1, Qun Li2
1School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, People's Republic of China.
Abstract:
Cardiac magnetic resonance imaging (MRI) usually requires a long acquisition time. The movement of the patients during MRI acquisition will produce image artifacts. Previous studies have shown that clear MR image texture edges are of great significance for pathological diagnosis. In this paper, a motion artifact reduction method for cardiac MRI based on edge enhancement network is proposed. Firstly, the four-plane normal vector adaptive fractional differential mask is applied to extract the edge features of blurred images. The four-plane normal vector method can reduce the noise information in the edge feature maps. The adaptive fractional order is selected according to the normal mean gradient and the local Gaussian curvature entropy of the images. Secondly, the extracted edge feature maps and blurred images are input into the de-artifact network. In this network, the edge fusion feature extraction network and the edge fusion transformer network are specially designed. The former combines the edge feature maps with the fuzzy feature maps to extract the edge feature information. The latter combines the edge attention network and the fuzzy attention network, which can focus on the blurred image edges. Finally, extensive experiments show that the proposed method can obtain higher peak signal-to-noise ratio and structural similarity index measure compared to state-of-art methods. The de-artifact images have clear texture edges.
Insights
This study introduces an edge enhancement network to reduce motion artifacts in cardiac MRI. The method improves image clarity and diagnostic value by preserving crucial texture edges.
Area of Science:
- Medical Imaging
- Image Processing
- Biomedical Engineering
Background:
- Cardiac MRI (magnetic resonance imaging) acquisition is lengthy, often leading to patient motion artifacts.
- Image artifacts in cardiac MRI can obscure pathological details, hindering accurate diagnosis.
- Clear texture edges in MR images are vital for identifying cardiac abnormalities.
Purpose of the Study:
- To develop a novel method for reducing motion artifacts in cardiac MRI.
- To enhance the clarity of texture edges in cardiac MRI for improved pathological diagnosis.
- To propose an edge enhancement network for de-artifacting cardiac MRI.
Main Methods:
- Utilized a four-plane normal vector adaptive fractional differential mask for edge feature extraction, reducing noise.
- Adaptive fractional order selection based on image gradients and Gaussian curvature entropy.
- Developed an edge enhancement network incorporating edge fusion feature extraction and transformer networks.
- Designed attention mechanisms within the transformer network to focus on blurred image edges.
Main Results:
- The proposed method significantly reduced motion artifacts in cardiac MRI.
- Achieved higher peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) compared to existing methods.
- Resulting de-artifacted images exhibited clear and well-defined texture edges.
Conclusions:
- The edge enhancement network effectively reduces motion artifacts in cardiac MRI.
- The method preserves and enhances critical texture edge information for diagnostic purposes.
- This approach offers a promising solution for improving the quality and diagnostic utility of cardiac MRI.
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