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This is a Experimental Study study.

September 17, 2025

Simultaneous reduction of noise and motion artifacts in brain MRI using deep learning.

Key Points

The method achieves an impressive SSIM score improvement from 0.72 to 0.95, indicating effective noise reduction.
Using 36,000 pairs of training data enhances the learning accuracy, demonstrating the method's robustness and adaptability.
The approach can effectively address noise and motion artifacts across various MRI imaging techniques, enhancing diagnostic quality.
As a post-processing solution, it enables application regardless of equipment or imaging method used.

Abstract

Motivation: The aim is to contribute to diagnosis by simultaneously reducing motion artifacts and noise in head MRI images using deep learning. Goal(s): The goal is to achieve high motion artifact and noise reduction in T1W, T2W, and FLAIR images, independent of artifact and noise levels. Approach: Simulation was used to create an image containing motion artifacts and noise, and deep learning was used to evaluate the removal effect. Results: The average SSIM between the ground troth and the input image was 0.72, and the SSIM between the ground troth and the output image using this method was 0.95, showing a high improvement effect. Impact: By 36,000 pairs of training data, we were able to increase the accuracy of the learning process. The advantage of this method is that it is post-processing and can be used regardless of the equipment or imaging method.

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Simultaneous reduction of noise and motion artifacts in brain MRI using deep learning.

Key Points

Abstract

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