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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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