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A Generative Adversarial Network technique for high-quality super-resolution reconstruction of cardiac magnetic resonance images by Ming Zhao; Yang Wei; Kelvin K.L. Wong is a Medicine article available to read on EtoBox.

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## Purpose: In this paper, we proposed a denoising super-resolution generative adversarial network (dnsrgan) method for high-quality super-resolution reconstruction of noisy cardiac magnetic resonance (cmr) images. ## Methods: The proposed method is based on feed-forward denoising convolutional neural network (dncnn) and srgan architecture. firstly, we used a feed-forward denoising neural network to pre-denoise the cmr image to ensure that the input is a clean image. secondly, we use the gradient penalty (gp) method to solve the problem of the discriminator gradient disappearing, which improves the convergence speed of the model. finally, a new loss function is added to the original srgan loss function to monitor gan gradient descent to achieve more stable and efficient model training, thereby providing higher perceptual quality for the super-resolution of cmr images. ## Results: We divided the tested cardiac images into 3 groups, each group of 25 images. then, we calculated the peak signal to noise ratio (psnr) /structural similarity (ssim) between ground truth (gt) and the images generated by super-resolution, used them to evaluate our model. we compared with the current widely u

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Author

Ming Zhao; Yang Wei; Kelvin K.L. Wong

Published

2021

Language

EN

Field

Medicine (Health Sciences)