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

TL;DR

This paper presents a novel neural network-based data-driven framework inspired by Reaction Diffusion systems for more accurate estimation and prediction of Time-Activity Curves in dynamic PET imaging, surpassing traditional compartmental models.

Contribution

It introduces an adaptive, neural network-based approach that directly calibrates diffusion and reaction parameters from data, improving modeling of complex biological dynamics in dPET.

Findings

Enhanced predictive accuracy over traditional models

Robustness in complex biological scenarios

Improved modeling of spatio-temporal radiopharmaceutical dynamics

Abstract

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.