Improving resolution of MR images with an adversarial network incorporating images with different contrast

By Ki Hwan Kim, Won-joon Do, Sung-Hong Park. 2018

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An overview of deep learning in medical imaging focusing on MRI (2018, background): “tuberculosis [296] CNN model for differentiating between tuberculous and pyogenic spondylitis in MR images.”
Generative Adversarial Network in Medical Imaging: A Review (2018, methodology): “The basic pix2pix framework has been used for low dose CT denoising (Wolterink et al., 2017b), MR reconstruction (Chen et al., 2018b; Kim et al., 2018; Dar et al., 2018b; Shitrit and Raviv, 2017), and PET denoising (Wang et al., 2018b).”
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2019, methodology): “Comparable approaches were used in the context of MRI reconstruction [73–78].”
Unsupervised Medical Image Translation With Adversarial Diffusion Models (2022, methodology): “To implement synthesis tasks from accelerated MRI scans [64], [65], fully-sampled MRI data were retrospectively undersampled 4-fold in two dimensions to attain low-resolution images at a 16x acceleration rate [64].”