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 ...
3.8 SIPID: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction.(ISBI 2019)[14] 3.1-3.7的方法均是CT的后处理,在图像域的操作,本文结合正弦域和图像域一起进行处理。实际上就是在CT未成为图像之前的投影数据上先进行一个正弦图插值(用超分辨网络),而后投影...
网络结构: 整体结构就如上图所示,part1生成器部分8个卷积层的CNN用于进行CT图像的重建;part2感知损失结构用预训练好的VGG-19将生成器生成的图像G(z)和ground truth(x)喂到VGG里用于特征提取,然后根据上式更新生成器的权重;part3判别器网络,结构如下图所示,6个卷积层,3*3的卷积核,最后一层没有sigmod。 最终...
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 ...
Medical 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 on the convolution operations,...
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...
Yang, Q., et al.: Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348–1357 (2018) Article Google Scholar Jiao, F., et al.: A dual-domain CNN-based network for CT reconstruction. IEEE ...
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...
Low-Dose CT Image Denoising with a Residual Multi-scale Feature Fusion Convolutional Neural Network and Enhanced Perceptual Loss This advanced CT image denoising network employs an attention mechanism for the feature extraction stage, facilitating the adaptive fusion of multi-scale local... FN Mazandaran...