Patlak Parametric Image Estimation from Dynamic PET Using Diffusion Model Prior
This work addresses image quality issues in dynamic PET for clinical and research applications, representing an incremental improvement through a novel hybrid method.
The authors tackled the problem of low-quality parametric images in dynamic PET due to ill-posed kinetic model fitting and limited data counts by proposing a diffusion model-based framework, which improved image quality as demonstrated on total-body PET datasets with varying dose levels.
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.