用BGO中的Cerenkov光预测飞行时间:一个三阶段的网络方法,先有多个定时内核
Xuhui Feng1, Hengjia Ran1, Huafeng Liu1
1State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, People's Republic of China.
Physics in medicine and biology
|August 13, 2024
概括
这项研究引入了一种新的三阶段深度学习网络,以提高飞行时间正子发射断层扫描 (TOF-PET) 图像质量. 该方法通过补偿混合光子信号的时间延迟来提高TOF的准确性,从而导致优异的图像重建.
科学领域:
- 医疗成像医学成像
- 核医学是一种核医学.
- 信号处理 信号处理
背景情况:
- pozitron发射断层扫描 (PET) 的图像质量通过飞行时间 (TOF) 的限制得到了提高.
- 来自BGO探测器的同时提示的Cerenkov和闪光发射提供了精确的TOF信息.
- 准确的TOF数据对于提高PET重建中的信号噪声比率至关重要.
研究的目的:
- 开发一种深度学习 (DL) 模型,用于在PET成像中准确地预测TOF.
- 为了弥补混合的Cerenkov和闪光子信号中的时间延迟.
- 在TOF-PET重建中提高图像质量.
主要方法:
- 开发了一个三阶段网络,结合了TOF预测的原始方法和带有偏差微调的DL模块.
- 数据根据信号上升时间分为25个类别,以生成TOF内核作为先前知识.
- 卷积神经网络 (CNN) 和变压器在网络的不同阶段被使用.
主要成果:
- 三级网络在半最大时提高了全宽度 (FWHM) 11.7 ps,在第十最大时提高了全宽度 (FWTM) 41.8 ps.
- 使用CNN和变压器架构,最佳性能为128.2ps的FWHM和286.6ps的FWTM.
- 数据增强进一步提高了FWHM的2.3 ps和FWTM的3.5 ps.
结论:
- 拟议的DL模型有效地弥补了TOF-PET混合信号的时间延迟.
- 将时间内核作为预先知识与DL模型集成,可以实现最佳的预测性能.
- 这种方法为利用Cerenkov信号的TOF-PET研究提供了重大进展.
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