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

Nihon Hoshasen Gijutsu Gakkai zasshi Pub Date : 2024-05-20 Epub Date: 2024-03-11 DOI:10.6009/jjrt.2024-1408

Motohira Mio, Nariaki Tabata, Tatsuo Toyofuku, Hironori Nakamura

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引用次数: 0

Abstract

Purpose: To investigate whether deep learning with high-pass filtering can be used to effectively reduce motion artifacts in magnetic resonance (MR) images of the liver.

Methods: The subjects were 69 patients who underwent liver MR examination at our hospital. Simulated motion artifact images (SMAIs) were created from non-artifact images (NAIs) and used for deep learning. Structural similarity index measure (SSIM) and contrast ratio (CR) were used to verify the effect of reducing motion artifacts in motion artifact reduction image (MARI) output from the obtained deep learning model. In the visual assessment, reduction of motion artifacts and image sharpness were evaluated between motion artifact images (MAIs) and MARIs.

Results: The SSIM values were 0.882 on the MARIs and 0.869 on the SMAIs. There was no statistically significant difference in CR between NAIs and MARIs. The visual assessment showed that MARIs had reduced motion artifacts and improved sharpness compared to MAIs.

Conclusion: The learning model in this study is indicated to be reduced motion artifacts without decreasing the sharpness of liver MR images.

查看原文本刊更多论文

[利用深度学习和高通滤波器减少肝脏磁共振成像中的运动伪影]。

目的：研究深度学习与高通滤波是否可用于有效减少肝脏磁共振（MR）图像中的运动伪影：研究对象为在本院接受肝脏磁共振检查的 69 名患者。模拟运动伪影图像（SMAIs）由非伪影图像（NAIs）创建，并用于深度学习。利用结构相似性指数（SSIM）和对比度（CR）来验证深度学习模型输出的运动伪影减少图像（MARI）中运动伪影的减少效果。在视觉评估中，对运动伪影图像（MAI）和 MARI 之间的运动伪影减少情况和图像清晰度进行了评估：MARIs的SSIM值为0.882，SMAIs的SSIM值为0.869。NAIs 和 MARIs 的 CR 没有明显的统计学差异。视觉评估显示，与 MAI 相比，MARI 减少了运动伪影，提高了清晰度：本研究中的学习模型可在不降低肝脏磁共振图像清晰度的情况下减少运动伪影。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Nihon Hoshasen Gijutsu Gakkai zasshi

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