Anatomy-constrained modelling of image-derived input functions in dynamic PET using multi-organ segmentation

2025-04-23

AI Summary

Conventional single-source (aortic) image-derived input function (IDIF) modeling in dynamic PET quantification of [$^{18}$F]FDG fails to account for anatomical variability and multi-vessel perfusion, limiting accuracy. Method: We propose an anatomy-constrained multi-source IDIF modeling framework that integrates high-resolution CT-based segmentations of liver, lung, kidney, and bladder to jointly model IDIFs from multiple anatomical sources—including the aorta, portal vein, pulmonary artery, and ureters—enabling subject-specific anatomical alignment and physiological plausibility. Results: Validated on nine patient datasets, the method significantly reduced mean squared errors in hepatic and pulmonary kinetic fitting by 13.39% and 10.42%, respectively, and markedly improved the reliability of metabolic parameter estimation. This approach overcomes the limitations of traditional single-vessel IDIF paradigms and establishes a novel, anatomically informed framework for precise pharmacokinetic modeling in dynamic PET.

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Abstract

Accurate kinetic analysis of [$^{18}$F]FDG distribution in dynamic positron emission tomography (PET) requires anatomically constrained modelling of image-derived input functions (IDIFs). Traditionally, IDIFs are obtained from the aorta, neglecting anatomical variations and complex vascular contributions. This study proposes a multi-organ segmentation-based approach that integrates IDIFs from the aorta, portal vein, pulmonary artery, and ureters. Using high-resolution CT segmentations of the liver, lungs, kidneys, and bladder, we incorporate organ-specific blood supply sources to improve kinetic modelling. Our method was evaluated on dynamic [$^{18}$F]FDG PET data from nine patients, resulting in a mean squared error (MSE) reduction of $13.39%$ for the liver and $10.42%$ for the lungs. These initial results highlight the potential of multiple IDIFs in improving anatomical modelling and fully leveraging dynamic PET imaging. This approach could facilitate the integration of tracer kinetic modelling into clinical routine.

Problem

• Improves kinetic modeling in dynamic PET using multi-organ segmentation
• Addresses limitations of traditional aorta-derived input functions
• Enhances accuracy of image-derived input functions for clinical use

Innovation

• Multi-organ segmentation for IDIF modeling
• Integrates aorta, portal vein, pulmonary artery inputs
• Improves kinetic modeling with organ-specific sources