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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