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基于全球空间信息和频道注意力,同时进行双追踪器PET成像的信号分离

Jingwan Fang¹, Fuzhen Zeng¹, Huafeng Liu²

¹State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China.

|May 29, 2024

概括

我们开发了FBPnet-Sep,这是一种用于同时进行双追踪器正子发射断层扫描 (PET) 成像的新型深度学习模型. 这种模型有效地分离信号,改善图像质量和双追踪器PET扫描的诊断信息.

科学领域:

医疗成像医学成像
核医学就是核医学.
医疗保健中的人工智能

背景情况:

同时的双追踪器正子发射断层扫描 (PET) 成像提供了全面的诊断信息,但在信号分离方面面临挑战.
从单个标记物中准确分离信号对于可靠地解释双标记物PET数据至关重要.

研究的目的:

开发和评估一个新的深度学习模型,FBPnet-Sep,用于在同时双追踪器PET成像中改进信号分离.
增强从双追踪器PET数据中重建单个追踪器图像和时间活动曲线.

主要方法:

提出了FBPnet-Sep模型,将全球空间信息和道注意力机制与FBP-Net.net集成在一起.
利用模拟动态PET数据进行全面的模型评估和比较.

主要成果:

在重建单个追踪器图像方面,FBPnet-Sep的性能优于现有的模型 (Sep-FBPnet,多任务CNN),具有优越的结构相似性和峰值信号噪声比.
该模型在低剂量场景中表现出稳健性,在多个标记剂组合中表现出有效性,衰变校正进一步提高了准确性.

结论:

FBPnet-Sep模型为同时进行双追踪器PET成像重建和信号分离提供了一个有希望的方法.
这种方法有可能提高核医学的诊断能力.

关键词:

注意力机制注意力机制深度学习是一种深度学习.双标志物PET是一种双标志物.重建重建的重建工作信号分离信号分离

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