Dual Low-b-value-driven U-shaped fusion GAN for synthesizing high-b-value prostate DWI

High-b-value (b = 2000 s/mm²) diffusion-weighted imaging (DWI) is vital for prostate disease detection and characterization due to superior tumor-to-background contrast, but its direct acquisition is time-consuming, technically demanding, and prone to noise and artifacts, limiting routine clinical use. This study aims to synthesize high-b-value prostate DWI from low-b-value data via a novel deep learning method. A dual low-b-value-driven U-shaped Fusion Generative Adversarial Network (UsFGAN) was proposed, integrating three core components: (1) U-Net-based dedicated subnetworks (with skip connections) for feature extraction from two low-b-values (b = 50/1000 sec/mm²); (2) Swin-Transformer with residual blocks (STRB) to capture local/long-range pixel dependencies; (3) hierarchical fusion network with multiple feature fusion blocks (MFFB) for adaptive multi-scale feature combination. Validation was done on a multi-center dataset of 280 subjects (6440 DWI slices). The proposed method outperformed state-of-the-art models (CycleGAN, Pix2Pix, DiscoGAN): peak signal-to-noise ratio = 36.14 dB, structural similarity index = 0.91, LPIPS = 0.09, FID = 8.87. Synthesized high-b-value DWI achieved 86.3% accuracy in prostate lesion detection. Radiologist qualitative evaluation confirmed synthesized images were comparable to real high-b-value scans in noise suppression, artifact reduction, and diagnostic acceptability. UsFGAN effectively leverages dual low-b-value complementary information to synthesize high-quality high-b-value prostate DWI. It exhibits superior performance and clinical diagnostic value, promising to reduce scan time and improve prostate disease assessment.