Abstract #4113

Prostate Cancer Detection Using High b-Value Diffusion MRI with a Multi-task 3D Residual Convolutional Neural Network

Guangyu Dan^1,2, Min Li³, Mingshuai Wang⁴, Zheng Zhong^1,2, Kaibao Sun¹, Muge Karaman^1,2, Tao Jiang³, and Xiaohong Joe Zhou^1,2,5

¹Center for MR Research, University of Illinois at Chicago, Chicago, IL, United States, ²Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States, ³Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China, ⁴Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China, ⁵Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States

Diffusion-weighted signal attenuation pattern contains valuable information regarding diffusion properties of the underlying tissue microstructures. With their extraordinary pattern recognition capability, deep learning (DL) techniques have a great potential to analyze diffusion signal decay. In this study, we proposed a 3D residual convolutional neural network (R3D) to detect prostate cancer by embedding the diffusion signal decay into one of the convolutional dimensions. By combining R3D with multi-task learning (R3D_MT), an excellent and stable prostate cancer detection performance was achieved in the peripheral zone (AUC of 0.990±0.008) and the transitional zone (AUC of 0.983±0.016).

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