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Patlak Parametric Image Estimation From Dynamic Pet Using Diffusion Model Prior.

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Patlak Parametric Image Estimation From Dynamic Pet Using Diffusion Model Prior.

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Patlak Parametric Image Estimation from Dynamic PET Using Diffusion Model Prior.

Ziqian Huang, Boxiao Yu, Siqi Li

|January 2, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel diffusion model for dynamic PET imaging, enhancing parametric image quality. The method improves estimations of physiological parameters, crucial for both research and clinical applications.

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Area of Science:

Medical Imaging
Nuclear Medicine
Computational Imaging

Background:

Dynamic Positron Emission Tomography (PET) allows quantitative physiological parameter estimation.
Parametric imaging in dynamic PET relies on kinetic modeling, but often yields low quality due to ill-posed fitting and limited data.
Existing methods struggle with noise and data limitations inherent in whole-body PET acquisitions.

Purpose of the Study:

To develop an advanced kinetic modeling framework for parametric image estimation in dynamic PET.
To leverage diffusion models as a prior to enhance the quality of parametric images derived from kinetic modeling.
To improve the accuracy and quality of voxel-wise physiological parameter maps in total-body dynamic PET.

Main Methods:

Proposed a diffusion model-based kinetic modeling framework for parametric image estimation.

Utilized the Patlak model as a specific kinetic model example.

Pre-trained the diffusion model's score function on static PET images to act as a prior for Patlak slope and intercept images, exploiting patch-wise similarity.

Integrated the kinetic model as a data-consistency constraint during the inference stage to guide image estimation.

Main Results:

Demonstrated the feasibility of the proposed diffusion model framework on total-body dynamic PET datasets.
Showcased promising performance in improving parametric image quality across different radiation dose levels.
The framework effectively leveraged prior information from static images to enhance dynamic PET parametric maps.

Conclusions:

The diffusion model-based kinetic modeling framework significantly enhances parametric image quality in dynamic PET.
This approach offers a robust solution to the challenges of low image quality in parametric imaging derived from kinetic modeling.
The method holds potential for improved quantitative analysis in both research and clinical settings using total-body dynamic PET.