Tracer kinetic modeling serves a vital role in diagnosis, treatment planning, tracer development and oncology, but burdens practitioners with complex and invasive arterial input function estimation (AIF). We adopt a physics-informed CycleGAN showing promise in DCE-MRI quantification to dynamic PET quantification. Our experiments demonstrate sound AIF predictions and parameter maps closely resembling the reference.

翻译：示踪剂动力学建模在疾病诊断、治疗规划、示踪剂开发及肿瘤学领域发挥着至关重要的作用，但其复杂的、具有侵入性的动脉输入函数估计过程为从业者带来了沉重负担。本研究将一种在动态对比增强磁共振成像量化中展现出潜力的物理信息循环一致性生成对抗网络，应用于动态正电子发射断层扫描的量化任务。实验结果表明，该方法能够生成准确的动脉输入函数预测值，其参数图与参考标准高度吻合。