Cross-modality PET image synthesis for Parkinson's Disease diagnosis: a leap from [¹⁸F]FDG to [¹¹C]CFT

Dopamine transporter [¹¹C]CFT PET is highly effective for diagnosing Parkinson's Disease (PD), whereas it is not widely available in most hospitals. To develop a deep learning framework to synthesize [¹¹C]CFT PET images from real [¹⁸F]FDG PET images and leverage their cross-modal correlation to distinguish PD from normal control (NC).

We developed a deep learning framework to synthesize [¹¹C]CFT PET images from real [¹⁸F]FDG PET images, and leveraged their cross-modal correlation to distinguish PD from NC. A total of 604 participants (274 with PD and 330 with NC) who underwent [¹¹C]CFT and [¹⁸F]FDG PET scans were included. The quality of the synthetic [¹¹C]CFT PET images was evaluated through quantitative comparison with the ground-truth images and radiologist visual assessment. The evaluations of PD diagnosis performance were conducted using biomarker-based quantitative analyses (using striatal binding ratios from synthetic [¹¹C]CFT PET images) and the proposed PD classifier (incorporating both real [¹⁸F]FDG and synthetic [¹¹C]CFT PET images).

Visualization result shows that the synthetic [¹¹C]CFT PET images resemble the real ones with no significant differences visible in the error maps. Quantitative evaluation demonstrated that synthetic [¹¹C]CFT PET images exhibited a high peak signal-to-noise ratio (PSNR: 25.0-28.0) and structural similarity (SSIM: 0.87-0.96) across different unilateral striatal subregions. The radiologists achieved a diagnostic accuracy of 91.9% (± 2.02%) based on synthetic [¹¹C]CFT PET images, while biomarker-based quantitative analysis of the posterior putamen yielded an AUC of 0.912 (95% CI, 0.889-0.936), and the proposed PD Classifier achieved an AUC of 0.937 (95% CI, 0.916-0.957).

By bridging the gap between [¹⁸F]FDG and [¹¹C]CFT, our deep learning framework can significantly enhance PD diagnosis without the need for [¹¹C]CFT tracers, thereby expanding the reach of advanced diagnostic tools to clinical settings where [¹¹C]CFT PET imaging is inaccessible.