Restoring image quality in accelerated PSMA PET across scanners and tracers using a generative adversarial network

Abstract Background: To evaluate the performance of a Channel Dense Dense U-Generative Adversarial Network (CDDU-GAN), in restoring the quality of four-fold accelerated Prostate-Specific Membrane Antigen (PSMA) PET acquisitions across different radiotracers and hybrid imaging platforms. Methods: This prospective study enrolled 84 patients who underwent either 18F-PSMA or 68Ga-PSMA PET imaging on PET/CT or PET/MR systems. Standard-dose listmode data (120s/bed for PET/CT, 240s/bed for PET/MR) were retrospectively subsampled to simulate a rapid-scan protocol (30s/bed and 60s/bed, respectively). The CDDU-GAN was trained to transform these low-count images to a quality equivalent to the standard-dose acquisitions. Objective image quality was quantified using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Subjective image quality was independently assessed by two blinded nuclear medicine physicians using a 5-point Likert scale for overall quality, lesion conspicuity, and diagnostic confidence. Statistical significance was determined using the Wilcoxon signed-rank test. Results: The CDDU-GAN framework yielded significant improvements in objective quality for the total cohort, increasing the mean PSNR from 46.8±7.5 to 48.2±6.6 (p<0.0001) and SSIM from 0.980±0.026 to 0.986±0.020 (p<0.0001). Performance gains were more pronounced for PET/CT datasets (PSNR increase: +3.3 dB for 18F-PSMA; +2.5 dB for 68Ga-PSMA) than for PET/MR. Inter-rater reliability for subjective analysis was substantial to almost perfect. The CDDU-GAN-enhanced images received significantly higher scores than unprocessed fast-scan images across all subjective metrics (p<0.05). Notably, the model increased the mean diagnostic confidence score for the entire cohort from 3.37±1.27 to 3.78±1.22, substantially closing the gap to the standard-dose score of 4.22±1.23 and restoring diagnostic utility in challenging cases. Conclusion: The proposed CDDU-GAN framework effectively restores image quality and diagnostic confidence in four-fold accelerated PSMA PET imaging. By successfully mitigating noise and improving image fidelity across different tracers and platforms, this deep learning approach holds significant potential to increase patient throughput, reduce motion artifacts, and improve patient comfort without a critical loss of diagnostic information.