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

Shen, Zhenrong; Wang, Jing; Huang, Haolin; Lu, Jiaying; Ge, Jingjie; Xiong, Honglin; Wu, Ping; Ju, Zizhao et al. · Eur J Nucl Med Mol Imaging · 2025

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Abstract

Dopamine transporter [<sup>11</sup>C]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 [<sup>11</sup>C]CFT PET images from real [<sup>18</sup>F]FDG PET images and leverage their cross-modal correlation to distinguish PD from normal control (NC). We developed a deep learning framework to synthesize [<sup>11</sup>C]CFT PET images from real [<sup>18</sup>F]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 [<sup>11</sup>C]CFT and [<sup>18</sup>F]FDG PET scans were included. The quality of the synthetic [<sup>11</sup>C]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 [<sup>11</sup>C]CFT PET images) and the proposed PD classifier (incorporating both real [<sup>18</sup>F]FDG and synthetic [<sup>11</sup>C]CFT PET images). Visualization result shows that the synthetic [<sup>11</sup>C]CFT PET images resemble the real ones with no significant differences visible in the error maps. Quantitative evaluation demonstrated that synthetic [<sup>11</sup>C]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 [<sup>11</sup>C]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 [<sup>18</sup>F]FDG and [<sup>11</sup>C]CFT, our deep learning framework can significantly enhance PD diagnosis without the need for [<sup>11</sup>C]CFT tracers, thereby expanding the reach of advanced diagnostic tools to clinical settings where [<sup>11</sup>C]CFT PET imaging is inaccessible.

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