UltraDenseNet for LowDose XRay Image Denoising in Cardiac CatheterBased Procedures The continuous development and prolonged use of Xray fluoroscopic imaging in cardiac catheterbased procedures is associated with increasing radiation dose to both patients and clinicians. Reducing the radiation dose leads to increased image noise and artifacts, which may reduce discernable image information. Therefore, advanced denoising methods for lowdose Xray images are needed to improve safety and reliability. Previous Xray imaging denoising methods mainly rely on domain filtration and iterative reconstruction algorithms and some remaining artifacts still appear in the denoised Xray images. Inspired by recent achievements of convolutional neural networks (CNNs) on feature representation in the medical image analysis field, this paper introduces an ultradense denoising network (UDDN) within the CNN framework for Xray image denoising in cardiac catheterbased procedures. After patchbased iterative training, the proposed UDDN achieves a competitive performance in both simulated and clinical cases by achieving higher peak signaltonoise ratio (PSNR) and signaltonoise ratio (SNR) when compared to previous CNN architectures.

Low Dose CT Image Denoising Using Multilevel Feature Fusion Network and Edge Constraints Lowdose computed tomography image denoising is a challenging task that has been studied by many researchers. Current denoising methods based on deep learning tend to produce a blur effect on the final results, especially at high noise levels, which are prone to oversmoothed edges and loss of details. In this paper, we propose a deep learning approach based on deep convolutional and edge constraints to mitigate these problems. Firstly, to avoid the loss of shallow layers details while obtaining semanticallyricher features information, we use dilated convolution instead of standard convolution, and fusion feature maps of different levels to aggregate information from different receptive field. Secondly, in order to improve the networku0027s ability to distinguish between noise and image content, we have designed an attention block to adaptively recalibrate the information relationship of the fusion feature maps. Finally, we incorporate edge prior knowledge into LDCT image denoising task, guiding the network to pay more attention on texture and structure information by edge constraints loss. Extensive experiments demonstrate that the proposed method achieves significant improvements over the stateoftheart methods.