{"title":"Cross-modality PET image synthesis for Parkinson’s Disease diagnosis: a leap from [18F]FDG to [11C]CFT","authors":"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","doi":"10.1007/s00259-025-07096-3","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><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><h3 data-test=\"abstract-sub-heading\">Methods</h3><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><h3 data-test=\"abstract-sub-heading\">Results</h3><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><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><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>","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":"65 1","pages":""},"PeriodicalIF":8.6000,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Nuclear Medicine and Molecular Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00259-025-07096-3","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
The European Journal of Nuclear Medicine and Molecular Imaging serves as a platform for the exchange of clinical and scientific information within nuclear medicine and related professions. It welcomes international submissions from professionals involved in the functional, metabolic, and molecular investigation of diseases. The journal's coverage spans physics, dosimetry, radiation biology, radiochemistry, and pharmacy, providing high-quality peer review by experts in the field. Known for highly cited and downloaded articles, it ensures global visibility for research work and is part of the EJNMMI journal family.