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16 March 2020 Synthesize CT from paired MRI of the same patient with patch-based generative adversarial network
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In current clinical practice, paired computed tomography (CT) providing electron density information for dose calculation and magnetic resonance imaging (MRI) providing molecular information for GTV delineation are acquired during radiation therapy planning of head cancer. Aimed to reduce repeatedly scanning procedures, we developed a patch-based deep learning approach to generate synthetic CT from paired MRI of the same patient. In this approach, 2D slices of MRI and CT would be divided into several overlap patches and sent to cycle-consistent generative adversarial network (CycleGAN) for training with a combination of multiple loss functions. For comparison, we also applied CycleGAN and pix2pix model using whole 2D slices as input. With IRB approval, a total number of 2542 paired MRI and CT images were collected in the experiment. Mean absolute error (MAE) and peak signal to noise ratio (PSNR) were used as evaluation metrics. The result showed that our proposed model performed best on both whole brain areas. We also provided the difference map between synthetic and real CT to give a visual evaluation of our proposed model.
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Yan Li, Jun Wei, Zhenyu Qi, Ying Sun, and Yao Lu "Synthesize CT from paired MRI of the same patient with patch-based generative adversarial network", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143W (16 March 2020);

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