标题： 超越传统参数化建模：动态PET成像中时间活动曲线估计和预测的数据驱动框架 显示英文标题

标题： Beyond Conventional Parametric Modeling: Data-Driven Framework for Estimation and Prediction of Time Activity Curves in Dynamic PET Imaging

摘要： 动态正电子发射断层成像（dPET）和时间-活度曲线（TAC）分析对于理解并量化放射性药物随时间和空间的生物分布至关重要。尽管传统的分区模型具有基础性作用，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。本研究介绍了一种创新的数据驱动神经网络框架，受反应扩散系统启发，旨在解决这些局限性。我们的方法通过自适应拟合dPET的TAC，能够直接从观测数据校准扩散系数和反应项，相较于传统方法，在预测准确性和鲁棒性方面有了显著提升，尤其是在复杂的生物场景中。通过更精确地建模放射性药物的空间-时间动力学，我们的方法推进了药代动力学和药效动力学过程的建模，为定量核医学带来了新的可能性。 显示英文摘要

摘要： 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.