Keywords: Machine Learning, Deep Learning, Radiology, Computed Tomography, Magnetic Resonance Imaging
Abstract: Synthetic CT image generation from MRI images is necessary to create radiotherapy plans without the need of co-registered MRI and CT scans. Our model based on GAN with cycle consistency permits unpaired image-to-image translation. Perceptual loss function term and coordinate convolutional layer were added to improve the quality of translated images. The proposed architecture was tested on paired MRI-CT dataset, where the synthetic CTs were compared to corresponding original CT images. The MAE between the synthetic CT images and the real CT scans is 63 HU computed inside of the true CTs body shape.
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