交叉噪音水平PET与持续的对抗性域概括进行无声化
Xiaofeng Liu1,2,3, Samira Vafay Eslahi1,2, Thibault Marin1,2,3
1Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States of America.
Physics in medicine and biology
|March 14, 2024
概括
这项研究引入了一种用于正电子发射断层扫描 (PET) 无噪声的新型域泛化技术,提高了不同噪声水平的图像质量. 该方法提高了阿尔茨海默氏症研究成像的准确性和一致性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射化学 放射化学是指辐射化学.
背景情况:
- pozitron 发射断层扫描 (PET) 图像的消噪对于减少差异至关重要.
- 深度学习模型在不同噪音水平的概括上扎,导致结果偏差.
- 域泛化提供了一个潜在的解决方案,以提高模型的稳定性.
研究的目的:
- 开发一个域泛化技术,用于在不同的噪声水平上强大的PET图像无声化.
- 为了最大限度地减少偏差,并改善基于深度学习的PET denoising模型中的概括性.
- 创建一个能够处理PET成像中任意噪声水平的模型.
主要方法:
- 用一个新的连续区分器 (CD) 进行对抗训练.
- 作为连续域标签,使用事件的分数来强制执行噪声级别不变的特征.
- 训练并测试了3D UNet模型与18F-MK6240 tau PET数据集上的CD在各种噪音水平上.
主要成果:
- 拟议的CD方法显著改善了denoising性能,减少了偏差和标准偏差.
- 在与阿尔茨海默氏症相关的区域和整个大脑图像中增强了结构相似度指数 (SSIM) 和峰值信号噪声比 (PSNR).
- 在训练分布之外的噪音水平上进行测试时,表现一致的性能改进.
结论:
- 本研究提出了第一个域泛化方法,用于交叉噪音级别PET无声化.
- 连续域泛化技术有效地解决了在不同噪声条件下性能降低的问题.
- 开发的方法有望提高PET成像在临床研究中的可靠性.
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