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.

研究中介绍了一种基于神经网络的数据驱动框架，通过适应性地拟合动态正电子发射断层图像中的时间 — 活动曲线，从观察数据中直接校准了扩散系数和反应项，特别是在复杂生物场景中，相较传统方法显著提高了预测精度和鲁棒性，并更准确地对放射性药物的时空动态进行建模，从而推进了药代动力学和药效学过程的定量核医学建模。

传统参数建模的超越：动态 PET 成像中时间活性曲线的数据驱动估计与预测框架

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

Deep learning models have demonstrated promising results in estimating
treatment effects (TEE). However, most of them overlook the variations in
treatment outcomes among subgroups with distinct characteristics. This
limitation hinders their ability to provide accurate estimations and treatment
recommendations for specific subgroups. In this study, we introduce a novel
neural network-based framework, named SubgroupTE, which incorporates subgroup
identification and treatment effect estimation. SubgroupTE identifies diverse
subgroups and simultaneously estimates treatment effects for each subgroup,
improving the treatment effect estimation by considering the heterogeneity of
treatment responses. Comparative experiments on synthetic data show that
SubgroupTE outperforms existing models in treatment effect estimation.
Furthermore, experiments on a real-world dataset related to opioid use disorder
(OUD) demonstrate the potential of our approach to enhance personalized
treatment recommendations for OUD patients.

本研究介绍了一种名为 SubgroupTE 的新型神经网络框架，该框架结合了亚群识别和治疗效果估计，通过考虑治疗反应的异质性，为每个亚群同时估计治疗效果，从而提高了治疗效果估计的准确性。在合成数据上的比较实验表明，SubgroupTE 在治疗效果估计方面优于现有模型。此外，针对与阿片类药物使用障碍（OUD）相关的真实数据的实验表明，我们的方法能够增强针对 OUD 患者的个性化治疗建议的潜力。