This study explored using deep learning (DL) to separate short-interval staggered 11C-CFT/18F-FDG brain PET images for Parkinson's disease, aiming to reduce scan waiting time. A total of 67 patients performing 11C-CFT and 18F-FDG brain PET scans on separate days were retrospectively enrolled. A Swin UNETR model was trained to generate pseudo 18F-FDG PET images from simulated dual-tracer sum images. The simulation assumed 18F-FDG was administered at 80, 100, 120, or 200min (∆t) after 11C-CFT injection. Compared to actual 18F-FDG images, the pseudo images showed high visual similarity across all ∆t intervals. Low average NMSE values (~0.0004) and high average SSIM values (0.9991-0.9993) were consistently achieved across all Δt groups. Bland & Altman analysis of the whole brain region demonstrated low average SUVR bias across all Δt groups remained within ±0.001. Region-wise correlation analysis revealed strong correlations between actual and pseudo 18F-FDG images across all Δt, with slopes ranging from 0.994 to 1.001, and all R2>0.99. SUVmean, LBR and SNR values for pseudo 18F-FDG images exhibited no statistically significant differences compared to actual 18F-FDG images (P>0.05). Dual-tracer PET images can be effectively separated using the DL model, yielding high-quality visual and semi-quantitative results when 18F-FDG is injected immediately after the 11C-CFT PET scan, thereby reducing patient wait time, improving patient comfort, and enhancing overall clinical efficiency.
Sun et al. (Wed,) studied this question.
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