{"ID":2826358,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.19584","arxiv_id":"2512.19584","title":"Patlak Parametric Image Estimation from Dynamic PET Using Diffusion Model Prior","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.","short_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, parame...","url_abs":"https://arxiv.org/abs/2512.19584","url_pdf":"https://arxiv.org/pdf/2512.19584v1","authors":"[\"Ziqian Huang\",\"Boxiao Yu\",\"Siqi Li\",\"Savas Ozdemir\",\"Sangjin Bae\",\"Jae Sung Lee\",\"Guobao Wang\",\"Kuang Gong\"]","published":"2025-12-22T17:11:33Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\",\"physics.med-ph\"]","methods":"[\"Diffusion Model\"]","has_code":false}