Low-dose CT denoising with convolutional neural network

Abstract

To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image quality. In this paper, we propose a noise reduction method for low-dose CT via deep neural network without accessing original projection data. A deep convolutional neural network is trained to transform low-dose CT images towards normal-dose CT images, patch by patch. Visual and quantitative evaluation demonstrates a competing performance of the proposed method.

Focusnet: An Attention-Based Fully Convolutional Network for Medical Image Segmentation

“…We demonstrate that our attention mechanism improves on an already state-of-the-art ResNet + SE architecture. For medical image processing tasks, existing works [9] [10] [11] demonstrate that pre-and post-processing such as, histogram equalization [9], image denoising [10] , contrast equalization [11], are crucial in achieving high accuracy. We demonstrate that our proposed architecture requires very minimal processing and performs well on images with varying conditions.…”

Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach

“…Second, we extend the approach by employing deep convolutional neural networks (CNNs). [29][30][31] Originally, CNNs were developed for image recognition task, 31,32 but recently, CNNs are also been applied to several medical applications, for example, diagnostic chest X-Rays, 33,34 fracture detection, [35][36][37] mammography, 38 low-dose X-ray tomography, [39][40][41] detection of osteoarthritis, 42,43 diagnosis of retinal diseases, 44 Alzheimer's disease diagnostics, [45][46][47] and MRI segmentation. 48 CNNs are also applied to the risk stratification of hypertension from PPG waveform data.…”

Low-Dose CT Image Denoising Based on Improved WGAN-gp

“…Since the features of LDCT images contain significant strip artifacts and noise, the purpose of the post-processing method is to remove artifacts and noise in the image. Chen et al [Chen, Zhang, Zhang et al (2017b)] proposed a fast dictionary learning method to improve the quality of the reconstructed CT images based on dictionary learning and sparse representation, but the dictionary learning method may introduce blurring and artifacts. Chen et al [Chen, Zhang, Zhang et al (2017a)] applied the non-local mean (NLM) method to CT image denoising.…”

“…So many work is carried out in this respect. Chen et al [Chen, Zhang, Zhang et al (2017b)] first applied convolutional neural network (CNN) to low-dose CT image denoising. Compared with traditional methods in visual effects and evaluation indicators, it shows certain superiority.…”

“…Compared with traditional methods in visual effects and evaluation indicators, it shows certain superiority. Chen et al [Chen, Zhang, Zhang et al (2017b)] proposed the residual encoder convolutional neural network (RED-CNN), and obtained the best results on the objective evaluation index, but the network complexity is high. These deep neural networks have achieved good results in low-dose CT image denoising.…”