Deep learning for synthesizing apparent diffusion coefficient maps of the prostate: A tentative study based on generative adversarial networks

We developed a supervised learning framework based on GAN in order to synthesize apparent diffusion coefficient maps (s-ADC) using full-FOV DWI images; zoomed-FOV ADC (z-ADC) served as the reference. Synthesized ADC using DWI with b=1000 mm2/s (S-ADCb1000) has statistically significant lower RMSE and higher PSNR, SSIM, and FSIM than s-ADCb50 and s-ADCb1500 (All P < 0.001). Both z-ADC and s-ADCb1000 had better reproducibility regarding quantitative ADC values in all evaluated tissues and better performance in tumor detection and classification than full-FOV ADC (f-ADC). A deep learning framework based on GAN is a promising method to synthesize realistic z-ADC sets with good image quality and accuracy in prostate cancer detection.