Parameter estimation of perfusion models in dynamic contrast-enhanced imaging: a unified framework for model...

Area of Science:

• Medical Imaging
• Oncology
• Physiology

Background:

• Patient follow-up in oncology relies on dynamic contrast-enhanced imaging.
• Accurate characterization of tumor physiology requires robust perfusion models.
• Current perfusion models lack clinical consensus, hindering reliable parameter estimation.

Purpose of the Study:

• To propose a unified framework for analyzing perfusion models and estimating their parameters.
• To enable the generation of reliable and relevant parametric images for oncology applications.
• To assess and compare widely used perfusion models in a clinical context.

Main Methods:

• Developed a methodological framework for model assessment and parameter estimation.
• Included global sensitivity analysis, identifiability analysis, parameter estimation, and model comparison.
• Applied the methodology to five common compartment models using patient data.

Main Results:

• Demonstrated the application of the unified framework to analyze five distinct perfusion models.
• Illustrated model behavior using dynamic imaging data from five abdominal tumor patients.
• Provided a systematic approach for evaluating perfusion models in clinical settings.

Conclusions:

• The proposed framework offers a systematic approach to assess perfusion models in oncology.
• Reliable parameter estimation is crucial for characterizing tumor physiology from dynamic imaging data.
• This work facilitates the selection and application of appropriate perfusion models for improved patient management.