Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligenceSynthetic CT (sCT) image generated from MRI by unsupervised deep learning models tends to have large errors around bone area. To generate better sCT image quality in bone area, we propose to add bony structure constrains in the loss function of the unsupervised CycleGAN model, and modify the single-channel CycleGAN to a multi-channel CycleGAN that takes Dixon constructed MR images as inputs. The proposed model has lowest mean absolute error compared with single-channel CycleGAN with different MRI images as input. We found that it can generate more accurate Hounsfield Unit and anatomy of bone in sCT.
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