Abstract
PurposeIn 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. MethodsThe 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. ResultsWe 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 used method: Bicubic ESRGAN and SRGAN, our method has better reconstruction quality and higher PSNR/SSIM score. ConclusionWe used DnCNN to denoise the CMR image, and then using the improved SRGAN to perform super-resolution reconstruction of the denoised image, we can solve the problem of high noise and artifacts that cause the cardiac image to be reconstructed incorrectly during super-resolution.
Highlights
Cardiac magnetic resonance (CMR) imaging plays an important role in the diagnosis of heart disease
We used denoising convolutional neural network (DnCNN) to denoise the CMR image, and using the improved SRGAN to perform super-resolution reconstruction of the denoised image, we can solve the problem of high noise and artifacts that cause the cardiac image to be reconstructed incorrectly during super-resolution
Denoising Super-resolution Generative Adversarial Network (DnSRGAN) method is proposed for high-quality super-resolution of noisy CMR images
Summary
Cardiac magnetic resonance (CMR) imaging plays an important role in the diagnosis of heart disease. The false triggering of ECG gating, patient arrhythmia and incomplete breath hold may cause artifacts or other noises during the acquisition of CMR images [1], which will greatly affect the cardiovascular image diagnosis [39] of patients.The suppression of noise is best handled in time during the acquisition process, but the hardware requirements are very demanding and the cost is relatively expensive. With the development of deep learning, image processing[36] methods such as denoising, super-resolution reconstruction and intelligent recognition of the collected images have become the focus of attention of most scholars. Yang et al used a CT image denoising method with Wasserstein distance and perceptual similarity based on Generative Adversarial Network (GAN) [7]. Perceptual similarity loss is compared with the perceptual features of the denoising output in the established feature space, and use the perceptual features of real images to suppress noise
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