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Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

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[Reduction of Motion Artifacts in Liver MRI Using Deep Learning with High-pass Filtering].

Motohira Mio¹, Nariaki Tabata¹, Tatsuo Toyofuku¹

¹Department of Radiology, Fukuoka University Chikushi Hospital.

Nihon Hoshasen Gijutsu Gakkai Zasshi

|March 10, 2024

Summary

Deep learning with high-pass filtering effectively reduces motion artifacts in liver MRI scans. This method enhances image sharpness without compromising diagnostic quality, improving visualization for medical professionals.

Keywords:

deep learning high-pass filtering liver magnetic resonance imaging motion artifact

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Area of Science:

Medical Imaging
Artificial Intelligence
Image Processing

Context:

Motion artifacts are a common problem in liver Magnetic Resonance Imaging (MRI).
These artifacts can degrade image quality and potentially affect diagnostic accuracy.
Developing effective methods to reduce motion artifacts is crucial for reliable liver MRI interpretation.

Purpose:

To evaluate the efficacy of a deep learning model incorporating high-pass filtering for reducing motion artifacts in liver MRI.
To assess the impact of this deep learning approach on image sharpness and diagnostic quality.

Summary:

A deep learning model was trained using simulated motion artifact images (SMAIs) derived from non-artifact images (NAIs).
The model generated motion artifact reduction images (MARIs) which were quantitatively assessed using Structural Similarity Index Measure (SSIM) and contrast ratio (CR).
Visual assessment compared MARIs against motion artifact images (MAIs), evaluating artifact reduction and image sharpness.

Impact:

The deep learning model successfully reduced motion artifacts in liver MRI.
Image sharpness was maintained or improved in the processed images (MARIs) compared to artifact-laden images (MAIs).
This technique offers a promising solution for enhancing the quality of liver MRI, aiding in more accurate diagnoses.