用于MR图像重建的POCS增强循环GAN
Yiran Li1,2, Hanlu Yang3, Danfeng Xie1,2
1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD 21201, USA.
Applied sciences (Basel, Switzerland)
|July 19, 2023
概括
这项研究引入了混合深度学习 (DL) 和传统方法,以实现更快的磁共振 (MR) 图像重建. 与现有方法相比,结合CycleGAN和POCS显著提高了MR图像重建质量.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算科学 计算科学
背景情况:
- 磁共振 (MR) 图像重建是计算密集的.
- 深度学习 (DL) 显示了加速MR图像重建的前景.
- 传统方法和DL方法通常被认为是独立的.
研究的目的:
- 研究传统的MR图像重建方法和DL的互补性.
- 开发一种混合方法,将DL和传统算法结合起来,以改进MR图像重建.
- 评估拟议的混合方法与现有的最先进技术的有效性.
主要方法:
- 开发了一种混合DL方法,集成了一个具有循环损失 (CycleGAN) 和投射对凸集 (POCS) 的生成对抗网络.
- POCS更新了最初的CycleGAN培训代的输出,为随后的培训提供了增强的数据.
- 混合方法的测试使用了部分样本的磁共振成像数据.
主要成果:
- 混合CycleGAN-POCS方法证明了优越的MR图像重建质量.
- 性能与其他DL方法 (U-Net,GAN,RefineGAN) 和压缩传感进行了比较.
- 提出的方法在所有测试方法中产生了最好的重建结果.
结论:
- 传统方法和DL在MR图像重建中是互补的.
- 混合CycleGAN-POCS方法为高质量,加速的MR成像提供了一个有希望的方向.
- 这项工作验证了医疗成像应用中代重建和深度学习之间的协同作用.
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