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

• URL: http://arxiv.org/abs/2504.17114v1
• Date: Wed, 23 Apr 2025 21:47:05 GMT
• Title: Anatomy-constrained modelling of image-derived input functions in dynamic PET using multi-organ segmentation
• Authors: Valentin Langer, Kartikay Tehlan, Thomas Wendler,
• Abstract summary: Accurate kinetic analysis of [$18$F]FDG distribution in dynamic positron emission tomography (PET) requires anatomically constrained modelling of image-derived input functions (IDIFs)<n>This study proposes a multi-organ segmentation-based approach that integrates IDIFs from the aorta, portal vein, pulmonary artery, and ureters.
• Score: 0.6359529834975265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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