Cross-Tracer Synthesis Model of ¹¹C-CFT and ¹⁸F-DOPA PET Images from ¹⁸F-FDG for Parkinson's Disease

Positron emission tomography (PET) with various tracers plays a critical role in Parkinson's disease (PD) studies. However, the clinical application of PET with novel or less-available tracers is limited by factors such as high cost and short half-life. To address this limitation, we developed a cross-tracer PET synthesis model based on diffusion model to directly synthesize cross-tracer PET images from widely used ¹⁸F-FDG PET images for PD studies. The network was initially trained on ¹⁸F-FDG and ¹¹C-CFT datasets, and subsequently fine-tuned on ¹⁸F-FDG and ¹⁸F-DOPA datasets. After anonymization, synthetic images were mixed with real ones and underwent visual assessment by radiologists. Quantitative analyses were further performed through voxel-wise comparisons and regional error analysis between synthetic and real images. Visual assessment indicated that the synthetic ¹¹C-CFT and ¹⁸F-DOPA PET images were comparable to real images in terms of quality, noise, and striatal conspicuity, with no significant differences observed in the error maps. Synthetic ¹¹C-CFT images achieved an average PSNR of 36.22 and an SSIM of 0.96, whereas ¹⁸F-DOPA images yielded values of 29.79 and 0.95, respectively. Bland-Altman analysis indicated high consistency between synthetic and real images. The average standardized uptake value (SUV) bias was 0.013 $\pm$ 0.032 SUV for ¹¹C-CFT and -0.175 $\pm$ 0.149 SUV for ¹⁸F-DOPA. The proposed cross-tracer PET synthesis model achieves comparable performance with ¹¹C-CFT or ¹⁸F-DOPA PET imaging for PD studies.