[10] H. Chen et al., "Low-dose CT with a residual encoder-decoder convolutional neural network", IEEE Trans. Image Process., vol. 36, no. 12, pp. 2524-2535, Dec. 2017. [11]Xin Yi,Paul Babyn:Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network.J. D...
One mitigation strategy involves using reduced radiation for low-dose CT (LDCT) imaging; however, this often results in noise artifacts that undermine diagnostic precision. To address this issue, a distinctive CT image denoising technique has been introduced that utilizes deep neural networks to ...
论文学习20“Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distan... 这篇文章就是WGAN的一个应用,2018 IEEE TRANSACTIONS ON MEDICAL IMAGING的文章,应用于低剂量CT的重建问题,文章模型总体结构如下: 作者应该是受到SRGAN的影响,将VGG的损失函数带到了网络里,不纯粹的使用MSE...
CT lung image denoisingMultiscale parallelConvolutionNeural networkDilated convolutionResidual learningThe continuous development and wide application of CT in medical practice have raised public concern over the associated radiation dose to the patient. However, reducing the radiation dose may result in ...
Image denoisingImage filteringMedical image reconstructionSemanticsTunable filtersLow-Dose CT (LDCT) scanning can greatly reduce the radiation damage to patients but would introduce serious noise and artifacts to CT images. The traditional deep learning based LDCT denoising methods are fundamentally based ...
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is...
Most of the existing low-dose computed tomography (LDCT) denoising algorithms, based on convolutional neural networks, are not interpretable enough due to a lack of mathematical basis. In the process of image denoising, the sparse representation based on a single dictionary cannot restore the texture...
Chen, H., et al.: Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36(12), 2524–2535 (2017) Article Google Scholar Yang, Q., et al.: Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perce...
Denoising low-dose computed tomography (CT) images is a critical task in medical image computing. Supervised deep learning-based approaches have made significant advancements in this area in recent years. However, these methods typically require pairs of low-dose and normal-dose CT images for traini...
Specifically, we incorporate three-dimensional (3D) volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve structural and texture information from normal-dose CT (...