Flow Matching-Based PET Image Reconstruction

2026-08-20

AI Summary

本文提出基于流匹配的PET图像重建方法，通过结合泊松似然引导和EM预处理器，实现数据一致性优化与流传播分离，提高不同剂量水平下的偏差-方差权衡。

Abstract

Generative models have shown strong potential for positron emission tomography (PET) image reconstruction. Although diffusion model-based reconstruction methods have demonstrated promising performance, they often require many reverse sampling steps with data-consistency updates incorporated into the sampling process. Flow matching offers an attractive alternative because it can directly estimate clean images from intermediate states, allowing data-consistency refinement to be separated from flow propagation. In this work, we proposed flow matching-based PET image reconstruction methods. We first established PET-FlowDPS by incorporating Poisson likelihood guidance with an expectation-maximization (EM)-based preconditioner into the FlowDPS framework. We then proposed a model-based PET reconstruction method that used a pretrained flow matching model as a prior, in which the flow-based prior, PET data refinement, and stochastic propagation were interpreted within an approximate Bayesian framework. Experimental results using [$^{\text{18}}\text{F}$]FDG brain PET datasets showed that the proposed method achieved better bias-variance trade-offs across different dose levels compared with other reference methods. These results demonstrated the potential of flow matching as a generative prior for quantitative PET image reconstruction.

Problem

• PET image reconstruction
• diffusion model
• flow matching
• data-consistency updates
• reverse sampling steps

Innovation

• flow matching
• Poisson likelihood guidance
• pretrained flow matching model
• approximate Bayesian framework