Abstract #0879

3D Super-resolution Prostate MRI using Generative Adversarial Networks and unpaired data

Yucheng Liu¹, Yulin Liu², Daniel Litwiller³, Rami Vanguri⁴, Michael Zenkay Liu⁵, Richard Ha⁵, Hiram Shaish⁵, and Sachin Jambawalikar⁵

¹Applied Physics and Applied Mathematics, Columbia University, New York, NY, United States, ²Information and Computer Engineering, Chung Yuan Christian University, Taoyuan, Taiwan, ³Global MR Applications and Workflow, GE Healthcare, New York, NY, United States, ⁴Data Science Institute, Columbia University, New York, NY, United States, ⁵Radiology, Columbia University Medical Center, New York, NY, United States

We developed a novel method to generate 3D isotropic super-resolution prostate MR images using a class of machine learning algorithms known as Generative Adversarial Networks (GANs). We use GANs to generate super-resolution images with 3D SVR image slices as inputs. Super-resolution is enforced as the discriminator network is trained to distinguish the output image from in-plane T2 FSE images, resulting in the generation of super-resolution images. We use unpaired GANs since slices of 3D SVR do not usually have corresponding super-resolution images. The result is a generated continuous 3D volume with super-resolution throughout all three planes in isotropic voxel size.

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