Flow Matching-Based PET Image Reconstruction

• URL: http://arxiv.org/abs/2608.20112v1
• Date: Thu, 20 Aug 2026 14:42:44 GMT
• Title: Flow Matching-Based PET Image Reconstruction
• Abstract summary: Flow matching can directly estimate clean images from intermediate states.<n>Generative models have shown strong potential for positron emission tomography (PET) reconstruction.
• Score: 4.971881974135159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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