Dynamic Positron Emission Tomography (dPET) imaging and Time-Activity Curve (TAC) analyses are essential for understanding and quantifying the biodistribution of radiopharmaceuticals over time and space. Traditional compartmental modeling, while foundational, commonly struggles to fully capture the complexities of biological systems, including non-linear dynamics and variability. This study introduces an innovative data-driven neural network-based framework, inspired by Reaction Diffusion systems, designed to address these limitations. Our approach, which adaptively fits TACs from dPET, enables the direct calibration of diffusion coefficients and reaction terms from observed data, offering significant improvements in predictive accuracy and robustness over traditional methods, especially in complex biological scenarios. By more accurately modeling the spatio-temporal dynamics of radiopharmaceuticals, our method advances modeling of pharmacokinetic and pharmacodynamic processes, enabling new possibilities in quantitative nuclear medicine.

翻译：动态正电子发射断层扫描（dPET）成像与时间-活度曲线（TAC）分析对于理解和量化放射性药物在时间与空间上的生物分布至关重要。传统的房室模型虽然具有基础性地位，但通常难以完全捕捉生物系统的复杂性，包括非线性动力学和变异性。本研究受反应扩散系统启发，提出了一种创新的数据驱动神经网络框架，旨在解决这些局限性。我们的方法通过自适应拟合dPET中的TAC，能够直接从观测数据中校准扩散系数与反应项，相比传统方法在预测精度与鲁棒性上实现显著提升，尤其在复杂生物场景中。通过更精确地建模放射性药物的时空动力学特性，本方法推进了药代动力学与药效动力学过程的建模，为定量核医学开辟了新的可能性。