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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