旋转变压器和UNET架构用于纠正磁共振图像重建中的运动工件
Md Biddut Hossain1, Rupali Kiran Shinde1, Shariar Md Imtiaz1
1Department of Information and Communication Engineering, Chungbuk National University, Cheongju-si 28644, Chungcheongbuk-do, Republic of Korea.
International journal of biomedical imaging
|May 10, 2024
概括
这项研究介绍了MACS-Net,这是一种使用Swin变压器来纠正MRI扫描中的运动文物的深度学习方法. 新方法显著改善图像质量,加速数据采集,性能优于现有方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 运动文物是磁共振成像 (MRI) 中的一个重大挑战,导致图像退化和延长扫描时间.
- 现有的运动校正技术往往难以处理复杂的运动模式,或需要额外的硬件.
研究的目的:
- 开发和评估一种基于深度学习的新方法,用于纠正MRI中的运动文物.
- 为了加速MRI数据采集和重建,同时保持高图像保真度.
主要方法:
- 开发了Swin网络 (MACS-Net) 的运动器件校正,在Unet架构中使用Swin变压器层.
- 采用了带有转移窗口的等级变压器来进行多尺度特征提取,以及用于增强分辨率的双重上采样技术.
- 在原始MRI数据集上训练并测试模型,该数据集包含各种运动工件和相应的地面真实图像.
主要成果:
- 与六种最先进的方法相比,MACS-Net显著减少了规范化根平均平方误差 (NRMSE),增加了结构相似度指数 (SSIM) 和改善了峰值信号噪声比 (PSNR).
- 对于短暂的运动 (<5秒),NRMSE从45.25%降至17.51%,SSIM从79.43%增加到91.72%,PSNR从18.24升至26.57dB.
- 对于延长运动 (5-10s),NRMSE从60.30%降至21.04%,SSIM从33.86%提高到90.33%,PSNR从15.64增加到24.99dB.
结论:
- MACS-Net有效地纠正MRI中的运动工件,从而大幅改善图像质量指标.
- 该方法显示了加速MRI采集和重建的潜力,而不会损害解剖细节.
- 深度学习方法显示出卓越的性能,特别是在处理简短和延长的运动场景时.
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