Advances and Challenges in Pharmacokinetic Modeling for PET Imaging: Compartment Models, Input Functions, and...

Pharmacokinetic modeling in Positron Emission Tomography (PET) imaging has become a cornerstone in cancer research, offering insights into tumor development and progression. These models facilitate the quantification of radiotracer distribution and metabolism, enabling precise measurement of physiological parameters essential for cancer diagnosis, staging, and treatment monitoring. However, accurate pharmacokinetic modeling depends on reliable input function acquisition and partial volume correction techniques to minimize biases in quantitative PET metrics. This review provides a comprehensive overview of current methodologies and advancements in pharmacokinetic modeling for PET oncology imaging. We discuss techniques for acquiring input functions, including arterial, venous, and image-derived input functions (IDIFs), along with population-based input functions (PBIFs). Their strengths, limitations, and clinical applications are critically evaluated. Additionally, we examine quantitative methods such as partial volume correction (PVC) that mitigate the spatial resolution limitations of PET, improving radiotracer quantification in small or heterogeneous tumors. Furthermore, we explore advanced kinetic modeling techniques, including compartmental models, graphical approaches, and data-driven methods, highlighting recent innovations such as machine learning and Bayesian modeling. Key areas for future research in PET pharmacokinetic modeling include integrating hybrid imaging modalities, developing robust patient-specific input functions, and leveraging machine learning to streamline modeling processes. These advancements aim to enhance the precision and clinical utility of PET imaging in oncology, leading to more personalized cancer treatment strategies.