Abstract #3414

Deep Learning Approaches Using Convolutional Neural Networks to Generate Synthetic CT from Spinal MRI for Radiotherapy Planning

Yang Zhang^1,2, Farouk Nouizi¹, Ning Lang³, Xiaoying Xing³, Yongye Chen³, Qizheng Wang³, Enlong Zhang³, Huishu Yuan³, Ke Nie², and Min-Ying Su¹

¹Department of Radiological Sciences, University of California, Irvine, CA, United States, ²Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States, ³Department of Radiology, Peking University Third Hospital, Beijing, China

Synopsis

A total of 27 patients receiving both spinal CT and MR for evaluation of back pain were identified for analysis. MR images and CT image were co-registered first, and the CT was used as ground truth for training a deep learning algorithm using MR images to generate synthetic CT. In this study, we implemented cycleGAN to generate these synthetic CT images from their corresponding MR slices. Five-fold cross validation was used to evaluate the performance of the trained model. Compared to the original images, the Mean Average Error was 27.63±11.51, and the Peak Signal-to Noise Ratio was 19.44±5.72.

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