发表机构
University of Florida; Nagoya Institute of Technology; RIKEN Center for Advanced Intelligence Project（佛罗里达大学; 名古屋工业大学; 理化学研究所先进智能项目中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出两种基于流匹配的PET图像重建方法，经[$^{\text{18}}\text{F}$]FDG脑部PET数据集实验，其在不同剂量水平下的偏差-方差权衡优于其他参考方法，展现出流匹配作为定量PET重建生成先验的潜力。
AI 中文摘要
生成模型在正电子发射断层扫描（PET）图像重建领域展现出强大潜力。尽管基于扩散模型的重建方法已表现出良好性能，但这类方法通常需要大量反向采样步骤，且采样过程中需融入数据一致性更新。流匹配提供了一种颇具吸引力的替代方案，因为它可直接从中间状态估计干净图像，使数据一致性优化与流传播相分离。本研究提出了基于流匹配的PET图像重建方法：首先，将泊松似然引导与基于期望最大化（EM）的预处理器融入FlowDPS框架，构建了PET-FlowDPS；随后，提出一种基于模型的PET重建方法，该方法使用预训练的流匹配模型作为先验，在近似贝叶斯框架内对基于流的先验、PET数据优化及随机传播进行解释。使用[$^{\text{18}}\text{F}$]FDG脑部PET数据集开展的实验结果表明，与其他参考方法相比，所提方法在不同剂量水平下均实现了更优的偏差-方差权衡。这些结果证明了流匹配作为定量PET图像重建生成先验的潜力。
英文摘要
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.
Comments10 pages, 8 figures
