用可逆与不可逆模块结合的网络，提前预测动态PET图像，缩短扫描时间。
Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules
- 设计可逆与不可逆模块融合的深度网络，从早期帧预测整体动态过程。
- 在模拟和临床数据上均实现高质量动态图像重建，保持关键代谢参数精度。
- 适合需要快速成像的临床场景，如肿瘤代谢监测或患者不适缓解。
动态正电子发射断层扫描（PET）可揭示示踪剂在生物体内的分布及生化反应动态过程，在临床上广泛应用。然而，长时间扫描易导致患者和医护人员不适。本文提出一种基于多模块深度学习框架的动态帧预测方法，通过早期帧预测动力学参数图像并重构完整动态PET图像，从而缩短扫描时间。在模拟数据验证中，该网络对动力学参数具有良好的预测性能，并能重建高质量动态PET图像；在临床数据实验中表现出良好泛化能力，表明该方法具备显著临床应用前景。
原文摘要 · Abstract (English)
Dynamic positron emission tomography (PET) images can reveal the distribution of tracers in the organism and the dynamic processes involved in biochemical reactions, and it is widely used in clinical practice. Despite the high effectiveness of dynamic PET imaging in studying the kinetics and metabolic processes of radiotracers. Pro-longed scan times can cause discomfort for both patients and medical personnel. This study proposes a dynamic frame prediction method for dynamic PET imaging, reduc-ing dynamic PET scanning time by applying a multi-module deep learning framework composed of reversible and irreversible modules. The network can predict kinetic parameter images based on the early frames of dynamic PET images, and then generate complete dynamic PET images. In validation experiments with simulated data, our network demonstrated good predictive performance for kinetic parameters and was able to reconstruct high-quality dynamic PET images. Additionally, in clinical data experiments, the network exhibited good generalization performance and attached that the proposed method has promising clinical application prospects.
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