Low-dose CT via convolutional neural network
TLDR
A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion, demonstrating a great potential of the proposed method on artifact reduction and structure preservation.Abstract:
In order to reduce the potential radiation risk, low-dose CT has attracted an increasing attention. However, simply lowering the radiation dose will significantly degrade the image quality. In this paper, we propose a new noise reduction method for low-dose CT via deep learning without accessing original projection data. A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion. Qualitative results demonstrate a great potential of the proposed method on artifact reduction and structure preservation. In terms of the quantitative metrics, the proposed method has showed a substantial improvement on PSNR, RMSE and SSIM than the competing state-of-art methods. Furthermore, the speed of our method is one order of magnitude faster than the iterative reconstruction and patch-based image denoising methods.read more
Citations
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Convolutional neural networks: an overview and application in radiology
TL;DR: A perspective on the basic concepts of convolutional neural network and its application to various radiological tasks is offered, and its challenges and future directions in the field of radiology are discussed.
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Deep Convolutional Neural Network for Inverse Problems in Imaging
TL;DR: In this paper, the authors proposed a deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems, which combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure.
Journal ArticleDOI
Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network
TL;DR: This work combines the autoencoder, deconvolution network, and shortcut connections into the residual encoder–decoder convolutional neural network (RED-CNN) for low-dose CT imaging and achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases.
Journal ArticleDOI
Generative Adversarial Networks for Noise Reduction in Low-Dose CT
TL;DR: Noise reduction improved quantification of low-density calcified inserts in phantom CT images and allowed coronary calcium scoring in low-dose patient CT images with high noise levels.
Journal ArticleDOI
Convolutional Neural Networks for Inverse Problems in Imaging: A Review
TL;DR: Recent experimental work in convolutional neural networks to solve inverse problems in imaging, with a focus on the critical design decisions is reviewed, including sparsity-based techniques such as compressed sensing.
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