跨模态PET图像合成用于帕金森病诊断:从[18F]FDG到[11C]CFT的飞跃

IF 8.6 1区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Journal of Nuclear Medicine and Molecular Imaging Pub Date : 2025-01-20 DOI:10.1007/s00259-025-07096-3
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
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引用次数: 0

摘要

目的多巴胺转运蛋白[11C]CFT PET对帕金森病(PD)的诊断非常有效,但在大多数医院尚未广泛应用。开发一个深度学习框架,从真实的[18F]FDG PET图像合成[11C]CFT PET图像,并利用它们的跨模态相关性来区分PD和正常对照(NC)。方法我们开发了一个深度学习框架,从真实的[18F]FDG PET图像合成[11C]CFT PET图像,并利用它们的跨模态相关性来区分PD和NC。共有604名参与者(274名PD患者和330名NC患者)接受了[11C]CFT和[18F]FDG PET扫描。通过与真实图像和放射科医生视觉评估的定量比较,评价合成[11C]CFT PET图像的质量。使用基于生物标志物的定量分析(使用合成[11C]CFT PET图像的纹状体结合比率)和所提出的PD分类器(包括真实[18F]FDG和合成[11C]CFT PET图像)对PD诊断性能进行评估。结果可视化结果显示,合成[11C]CFT PET图像与真实图像相似,误差图无明显差异。定量评价表明,合成[11C]CFT PET图像在不同单侧纹状体亚区表现出较高的峰值信噪比(PSNR: 25.0 ~ 28.0)和结构相似性(SSIM: 0.87 ~ 0.96)。放射科医生基于合成[11C]CFT PET图像的诊断准确率为91.9%(±2.02%),而基于生物标志物的后壳核定量分析的AUC为0.912 (95% CI, 0.889-0.936),所提出的PD分类器的AUC为0.937 (95% CI, 0.916-0.957)。结论通过缩小[18F]FDG和[11C]CFT之间的差距,我们的深度学习框架可以在不需要[11C]CFT示踪剂的情况下显著提高PD诊断,从而将先进的诊断工具扩展到无法获得[11C]CFT PET成像的临床环境中。
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Cross-modality PET image synthesis for Parkinson’s Disease diagnosis: a leap from [18F]FDG to [11C]CFT

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.

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来源期刊
CiteScore
15.60
自引率
9.90%
发文量
392
审稿时长
3 months
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.
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