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Abstract #0245

Deep Shoulder CT Image Synthesis from MR via Context-aware 2.5D Generative Adversarial Networks

Yucheng Liu1, Yulin Liu2, Michael Z. Liu1, Pawas S. Shukla1, Richard Ha1, Tim Duong3, Sachin R. Jambawalikar1, and Tony T. Wong1
1Radiology, Columbia University Irving Medical Center, New York, NY, United States, 2Information and Computer Engineering, Chung Yuan Christian University, Taoyuan City, Taiwan, 3Radiology, Stony Brook Medicine, Stony Brook, NY, United States

We developed a context-aware 2.5D Generative Adversarial Network (GAN) to generate synthetic CT images from MRI. Adjacent 2D slices with in plane matrix of 512 x 512 and user defined slice context (from 3 to 41-slices) were provided as input. This allows the network to learn out-of-plane information for the slice of interest thereby alleviating the intensity discontinuity problem seen in 2D networks. In addition, this approach uses less GPU memory than a 3D GAN. Our results indicated that the network trained with larger number of adjacent slices outperform the fewer slice network.

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