用扩散模型把FDG PET数据转成DOPA PET数据，提升肿瘤成像效果。
A Prior-Guided Joint Diffusion Model in Projection Domain for PET Tracer Conversion
- 基于投影域的联合扩散模型，利用先验引导生成过程。
- 合成的DOPA sinogram质量显著提升，还原度更高。
- 适合想低成本获取高特异性PET影像的研究者使用。
正电子发射断层扫描（PET）广泛用于评估代谢活性，但受限于放射性示踪剂的可用性。18F-FDG是最常用的示踪剂，但在某些肿瘤中效果有限。相比之下，18F-DOPA对神经内分泌肿瘤和神经系统疾病具有更高的特异性。然而，其合成复杂且运输时间受限，临床应用受限。在扫描仪采集的原始数据中，影子图（sinogram）是常用表示形式。在投影域建模可更直接利用原始信息，减少图像重建中的误差积累。受此启发，本文提出一种先验引导的联合扩散模型（PJDM），用于将18F-FDG PET影子图转换为18F-DOPA PET影子图。推理时，首先通过高阶混合采样器生成初始合成的18F-DOPA PET影子图，再将其退化作为额外条件，指导迭代精炼过程。实验结果表明，PJDM有效提升了影子图质量和最终合成结果。代码已公开：https://github.com/yqx7150/PJDM。
原文摘要 · Abstract (English)
Positron emission tomography (PET) is widely used to assess metabolic activity, but its application is limited by the availability of radiotracers. 18F-labeled fluorodeoxyglucose (18F-FDG) is the most commonly used tracer but shows limited effectiveness for certain tumors. In contrast, 6-18F-fluoro-3,4-dihydroxy-L-phenylalanine (18F-DOPA) offers higher specificity for neuroendocrine tumors and neurological disorders. However, the complexity of its synthesis process and constraints on transportation time have limited its clinical application. Among different forms of raw data acquired by the scanner, sinogram is a commonly used representation in PET imaging. Therefore, modeling in projection domain enables more direct utilization of the original information, potentially reducing the accumulation errors during the image reconstruction process. Inspired by these factors, this study proposes a prior-guided joint diffusion model (PJDM) for transforming 18F-FDG PET sinograms into 18F-DOPA PET sinograms. During inference, an initial synthetic 18F-DOPA PET sinogram is first generated using a higher-order hybrid sampler. This sinogram is then degraded and serves as an additional condition to guide the iterative refinement process. Experimental results demonstrated that PJDM effectively improved both sinogram quality and the final synthetic outcomes. The code is available at: https://github.com/yqx7150/PJDM.
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