Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 (82-Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary artery diseases. However, the high cross-frame distribution variation due to rapid tracer kinetics poses a considerable challenge for inter-frame motion correction, especially for early frames where intensity-based image registration techniques often fail. To address this issue, we propose a novel method called Temporally and Anatomically Informed Generative Adversarial Network (TAI-GAN) that utilizes an all-to-one mapping to convert early frames into those with tracer distribution similar to the last reference frame. The TAI-GAN consists of a feature-wise linear modulation layer that encodes channel-wise parameters generated from temporal information and rough cardiac segmentation masks with local shifts that serve as anatomical information. Our proposed method was evaluated on a clinical 82-Rb PET dataset, and the results show that our TAI-GAN can produce converted early frames with high image quality, comparable to the real reference frames. After TAI-GAN conversion, the motion estimation accuracy and subsequent myocardial blood flow (MBF) quantification with both conventional and deep learning-based motion correction methods were improved compared to using the original frames.

翻译：在采用铷-82（82-Rb）心肌灌注成像的动态心脏正电子发射断层扫描（PET）中，帧间运动会干扰心肌血流量（MBF）的定量分析及冠状动脉疾病的诊断准确性。然而，由于快速示踪剂动力学导致的跨帧分布显著变化，对帧间运动校正构成了重大挑战，尤其在早期帧中，基于强度的图像配准技术常失效。为解决此问题，我们提出一种名为时间与解剖信息引导的生成对抗网络（TAI-GAN）的新方法，采用全到一映射将早期帧转换为与最后一个参考帧示踪剂分布相似的图像。TAI-GAN包含一个特征级线性调制层，用于编码由时间信息生成的通道级参数，以及带有局部偏移的粗略心脏分割掩膜（作为解剖信息）。所提方法在临床82-Rb PET数据集上进行了评估，结果表明TAI-GAN能生成与真实参考帧图像质量相当的高质量转换早期帧。经TAI-GAN转换后，采用传统及基于深度学习的运动校正方法，其运动估计精度及后续心肌血流量（MBF）定量分析均较原始帧有所提升。