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Purpose <p>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).</p> Methods <p>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).</p> Results <p>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).</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年1月20日

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  • Zhenrong Shen,
  • Jing Wang,
  • Haolin Huang,
  • Jiaying Lu,
  • Jingjie Ge,
  • Honglin Xiong,
  • Ping Wu,
  • Zizhao Ju,
  • Huamei Lin,
  • Yuhua Zhu,
  • Yunhao Yang,
  • Fengtao Liu,
  • Yihui Guan,
  • Kaicong Sun,
  • Jian Wang,
  • Qian Wang,
  • Chuantao Zuo

摘要

Purpose

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).

Methods

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).

Results

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).

Conclusion

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.