Low-dose CT via convolutional neural network.

By Hu Chen, Yi Zhang, Wei-hua Zhang, Peixi Liao, Ke Li, Jiliu Zhou, Ge Wang. Biomedical Optics Express, 2017

688 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f630d40395acabe2b0030c96a0418935c8aac0c1

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine (2018, background): “Data mining regarding relevant issues, including radiation dose [38].”
Convolutional Neural Networks for Inverse Problems in Imaging: A Review (2017, methodology): “This formulation is used in most of the works we surveyed, [6], [7], [11], [12], [18], [19], [23], [25], [26], despite the fact that several raise questions about whether it is the best choice [25], [34].”
Convolutional Neural Network based Metal Artifact Reduction in X-ray Computed Tomography (2017, background): “The CNN training is to ﬁnd a function H : R s × t × 3 → R s × t that minimizes the following cost function [30]: where (cid:107) · (cid:107) F is the Frobenius norm.”
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT (2017, background): “This work was followed by many extensions [9], [10], [11], [12].”
LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT (2017, positive): “2, we use a three layer CNN (3Layer-CNN) [38] to”
Deep learning with convolutional neural network in radiology (2018, positive): “reported that quality of CT images could be improved in terms of qualitative and quanti‐ tative analyses with less time by using models based on deep learning techniques [71, 72].”
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction (2018, methodology): “To address this challenge, various noise reduction algorithms were proposed for LDCT, which can be categorized into the following categories: 1) sinogram filtration [8, 9, 10], 2) iterative reconstruction [11, 12, 13], and 3) image post-processing [14, 15, 16, 17].”
Deep Convolutional Neural Network for Inverse Problems in Imaging (2016, methodology): “work [46] studies the use of a CNN to postprocess a single”
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network (2018, positive): “Importantly, the proposed strategy is expected to be applicable to all existing 2D models, such as those discussed in [5], [6], [8], [9].”
NETT: solving inverse problems with deep neural networks (2018, background): “rms well in practice for a sparse tomographic data problem. 1.3 Comparison to previous work Very recently, several deep learning approaches for inverse problems have been developed (see, for example, [1, 3, 8, 23, 24, 39, 21, 36, 40, 41]). In all these approaches, a reconstruction network Φrec(V,·):”
The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence (2018, background): “One application is image-space-based reconstructions in which convolutional neural networks are trained with low-dose CT images to reconstruct routine-dose CT images [17, 114, 115].”
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network (2017, background): “Since then, several pioneering deep learning approaches for low-dose CT have been proposed by many researchers [14], [15], [16], [17], [18], [19], [20], [21], [22], [23].”
Generative Adversarial Networks for Noise Reduction in Low-Dose CT (2017, methodology): “The methods proposed in [13]–[15] showed good quantitative noise reduction properties.”
Convolutional neural networks: an overview and application in radiology (2018, background): “Two previous studies showed that low-dose and ultra-low-dose CT images could be effectively denoised using deep learning [52, 53].”