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Abstract #4124

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

lei hu1, Jungong Zhao1, Caixia Fu2, and Thomas Benkert3
1Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixt, 上海, China, 2MR Application Development, Siemens Shenzhen magnetic Resonance Ltd, shenzhen, China, 3MR Application Predevelopment, Siemens Healthcare, Erlangen, Gernmany, Erlangen, Germany

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.

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