Transformer-Enhanced Generative Adversarial Networks for Improving MR Image Quality in Prostate Imaging

Consistently high-quality and standardized prostate MRI is crucial for reliable PI-RADS interpretation and accurate prostate cancer detection, particularly given the heterogeneity in acquisition protocols across institutions. In this study, we develop and validate an innovative anatomy-aware pyramid-spatially adaptive normalization and transformer-enhanced CycleGAN (PST-CyGAN) framework for standardizing and enhancing prostate MRI quality in large-scale multicenter datasets. This study included 2,207 patients for model training and 497 for validation from 8 centers, all of whom underwent prostate MRI between January 2017 and May 2025. PST-CyGAN was built upon CycleGAN with integrated anatomical attention, pyramid-spatially adaptive normalization, and transformer enhancement. High-resolution T2-weighted imaging (HR-T2WI; resolution: 0.35 × 0.35 × 2.0 mm) with quality control was acquired from 941 patients to ensure the model’s ability to generate high-quality images. Ablation tests were conducted comparing PST-CyGAN against three state-of-the-art (SOTA) models: CycleGAN, S-CyGAN, and PS-CyGAN. For clinical validation, five blinded radiologists evaluated the image quality of PST-CyGAN-generated HR-T2WI using a 10-point PI-QUAL scoring system. Changes in PI-QUAL scores compared to standard-resolution (SR) T2WI were analyzed using Bland–Altman plots and paired Wilcoxon tests. In ablation testing, PST-CyGAN consistently outperformed all three SOTA models across all metrics: PSNR (29.3 ± 0.5), SSIM (0.558 ± 0.116), FCS (0.915 ± 0.028), and LPIPS (0.169 ± 0.056) (all P  < 0.01). Improvements were most notable in initially low-quality scans (PI-QUAL < 6). In multi-reader validation, PST-CyGAN trained with quality-controlled data achieved the highest clinical benefit, upgrading PI-QUAL scores in 38% of cases and downgrading in 20%, compared to 11% upgrades and 69% downgrades without quality control ( P  < 0.01). Prospective testing demonstrated PI-QUAL score improvements of 25.5%, 20.0%, and 29.1% for voxel sizes of 1.5×1.5×3.3 mm, 0.7×0.7×3.3 mm, and 0.4×0.4×2.0 mm, respectively, confirming robust generalizability. PST-CyGAN significantly and reproducibly enhances prostate T ₂ WI quality across diverse centers and acquisition protocols, exceeding all SOTA models on quantitative metrics. By generating standardized, high-resolution synthetic T ₂ WI—especially from initially low-quality scans—it reduces inter-site variability, supports consistent PI-RADS application, and may improve prostate MRI interpretation in clinical workflows.