Patlak Parametric Image Estimation from Dynamic PET Using Diffusion Model Prior

• URL: http://arxiv.org/abs/2512.19584v1
• Date: Mon, 22 Dec 2025 17:11:33 GMT
• Title: Patlak Parametric Image Estimation from Dynamic PET Using Diffusion Model Prior
• Authors: Ziqian Huang, Boxiao Yu, Siqi Li, Savas Ozdemir, Sangjin Bae, Jae Sung Lee, Guobao Wang, Kuang Gong,
• Abstract summary: Parametric imaging in dynamic PET requires kinetic modeling to estimate physiological parameters.<n>The proposed framework was evaluated on total-body dynamic PET datasets with different dose levels.
• Score: 9.8798936444224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic PET enables the quantitative estimation of physiology-related parameters and is widely utilized in research and increasingly adopted in clinical settings. Parametric imaging in dynamic PET requires kinetic modeling to estimate voxel-wise physiological parameters based on specific kinetic models. However, parametric images estimated through kinetic model fitting often suffer from low image quality due to the inherently ill-posed nature of the fitting process and the limited counts resulting from non-continuous data acquisition across multiple bed positions in whole-body PET. In this work, we proposed a diffusion model-based kinetic modeling framework for parametric image estimation, using the Patlak model as an example. The score function of the diffusion model was pre-trained on static total-body PET images and served as a prior for both Patlak slope and intercept images by leveraging their patch-wise similarity. During inference, the kinetic model was incorporated as a data-consistency constraint to guide the parametric image estimation. The proposed framework was evaluated on total-body dynamic PET datasets with different dose levels, demonstrating the feasibility and promising performance of the proposed framework in improving parametric image quality.

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