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

Area of Science:

• Nuclear medicine
• Radiochemistry
• Oncology

Background:

• Positron Emission Tomography (PET) imaging is crucial for cancer research, providing insights into tumor dynamics.
• Accurate pharmacokinetic modeling in PET requires reliable input function data and partial volume correction (PVC) to minimize quantification biases.

Purpose of the Study:

• To provide a comprehensive review of current methodologies and advancements in pharmacokinetic modeling for PET oncology imaging.
• To critically evaluate various input function acquisition techniques and their clinical applications.
• To examine quantitative methods like PVC for improving radiotracer quantification in challenging tumor scenarios.

Main Methods:

• Review of techniques for input function acquisition: arterial, venous, image-derived input functions (IDIFs), and population-based input functions (PBIFs).
• Evaluation of partial volume correction (PVC) methods to address PET spatial resolution limitations.
• Exploration of advanced kinetic modeling approaches: compartmental, graphical, and data-driven methods, including machine learning and Bayesian modeling.

Main Results:

• Discussion of the strengths, limitations, and clinical utility of different input function strategies.
• Assessment of PVC techniques for enhancing quantification accuracy in small or heterogeneous tumors.
• Highlighting innovations in kinetic modeling, such as AI-driven approaches.

Conclusions:

• Future research should focus on integrating hybrid imaging, developing patient-specific input functions, and utilizing machine learning for streamlined modeling.
• Advancements in PET pharmacokinetic modeling promise to increase precision and clinical utility in oncology.
• Enhanced modeling will contribute to more personalized cancer treatment strategies.