Abstract #1336

Deep learning for synthesizing high-b-value DWI of the prostate: A tentative study based on generative adversarial networks

lei hu¹, jungong Zhao^1,2, Caixia fu³, and Thomas Benkert⁴

¹Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixt, 上海, China, ²Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixt, shanghai, China, ³MR Application Development, Siemens Shenzhen magnetic Resonance Ltd, shanghai, China, ⁴MR Application Predevelopment, Siemens Healthcare, Erlangen, Gernmany, Erlangen, Germany

A deep learning framework based on a generative adversarial network (GAN) to synthesize high-b-value DWI (syn-DWIb1500) with high quality using the acquired standard b-value DWI (a-DWIb800-1000) was developed. Reader ratings for image quality and PCa detection were performed on the a-DWI b1500, syn-DWIb1500, and optimized syn-DWIb1500 sets. Wilcoxon signed-rank tests and MRMC-ROC were used to compare the readers’ scores and diagnostic capabilities of each DWI set, respectively. Optimized syn-DWIb1500 resulted in significantly better image quality (all P≤0.001) and a higher mean AUC than a-DWIb1500 and cal-DWIb1500 (all P≤0.042).

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