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Magnetic Resonance Imaging

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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.

Physics in Medicine and Biology

|March 27, 2024

Summary

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.

Keywords:

MRI de-artifact adaptive fractional differential edge enhancement four-plane normal vector transformer

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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.