Single magnetic resonance image super-resolution using generative adversarial network

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Super-Resolution is the process of converting given low-resolution images into corresponding high-resolution ones. The resolution enhancement process applied to medical images can potentially improve diagnostic accuracy for a variety of conditions and reduce imaging scan time. Recent improvements in computational tools have made deep learning methods popular for various image processing techniques including resolution enhancement. In this thesis, we consider the image super-resolution problem of the brain and cardiac MRI datasets. To increase the spatial resolution of these medical images, we have adapted a Generative Adversarial Network (GAN) model where the generator has a DenseNet type structure and the discriminator is based on the U-Net model. We have used a combination of loss functions to ensure the generated images are consistent with ground truth. To train and validate the model, we have used four different datasets consisting of brain and cardiac MRI. Promising qualitative and quantitative results are provided.
Medical image super-resolution, Magnetic Resonance Image, Generative Adversarial Network, Real-ESRGAN