Journal ArticleDOI
Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks.
Hu Zhanli,Jiang Changhui,Sun Fengyi,Qiyang Zhang,Yongshuai Ge,Yang Yongfeng,Xin Liu,Hairong Zheng,Dong Liang +8 more
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TLDR
This work trained a generative adversarial network with Wasserstein distance and mean squared error (MSE) loss, called m-WGAN, to remove artifacts and obtain high-quality CT dental images in a clinical dental CT examination environment and is the first deep learning architecture used with a commercial cone-beam dental CT scanner.Abstract:
Purpose In recent years, health risks concerning high-dose x-ray radiation have become a major concern in dental computed tomography (CT) examinations. Therefore, adopting low-dose computed tomography (LDCT) technology has become a major focus in the CT imaging field. One of these LDCT technologies is downsampling data acquisition during low-dose x-ray imaging processes. However, reducing the radiation dose can adversely affect CT image quality by introducing noise and artifacts in the resultant image that can compromise diagnostic information. In this paper, we propose an artifact correction method for downsampling CT reconstruction based on deep learning. Method We used clinical dental CT data with low-dose artifacts reconstructed by conventional filtered back projection (FBP) as inputs to a deep neural network and corresponding high-quality labeled normal-dose CT data during training. We trained a generative adversarial network (GAN) with Wasserstein distance (WGAN) and mean squared error (MSE) loss, called m-WGAN, to remove artifacts and obtain high-quality CT dental images in a clinical dental CT examination environment. Results The experimental results confirmed that the proposed algorithm effectively removes low-dose artifacts from dental CT scans. In addition, we showed that the proposed method is efficient for removing noise from low-dose CT scan images compared to existing approaches. We compared the performances of the general GAN, convolutional neural networks, and m-WGAN. Through quantitative and qualitative analysis of the results, we concluded that the proposed m-WGAN method resulted in better artifact correction performance preserving the texture in dental CT scanning. Conclusions The image quality evaluation metrics indicated that the proposed method effectively improves image quality when used as a postprocessing technique for dental CT images. To the best of our knowledge, this work is the first deep learning architecture used with a commercial cone-beam dental CT scanner. The artifact correction performance was rigorously evaluated and demonstrated to be effective. Therefore, we believe that the proposed algorithm represents a new direction in the research area of low-dose dental CT artifact correction.read more
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Journal ArticleDOI
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction.
Hongming Shan,Atul Padole,Fatemeh Homayounieh,Uwe Kruger,Ruhani Doda Khera,Chayanin Nitiwarangkul,Chayanin Nitiwarangkul,Mannudeep K. Kalra,Ge Wang +8 more
TL;DR: In this article, a modularized neural network for low-dose CT (LDCT) was proposed and compared with commercial iterative reconstruction methods from three leading CT vendors, and the learned workflow allows radiologists-in-the-loop to optimize the denoising depth in a task-specific fashion.
Journal ArticleDOI
DPIR-Net: Direct PET Image Reconstruction Based on the Wasserstein Generative Adversarial Network
Hu Zhanli,Xue Hengzhi,Qiyang Zhang,Juan Gao,Na Zhang,Sijuan Zou,Yueyang Teng,Xin Liu,Yang Yongfeng,Dong Liang,Xiaohua Zhu,Hairong Zheng +11 more
TL;DR: This article proposes the use of a direct PET image reconstruction network (DPIR-Net) using an improved Wasserstein generative adversarial network (WGAN) framework to enhance image quality and proposes a loss function that effectively solves the problem of excessive smoothness and loss of detailed information in traditional network image reconstruction.
Journal ArticleDOI
CaGAN: A Cycle-Consistent Generative Adversarial Network With Attention for Low-Dose CT Imaging
Huang Zhiyuan,Zixiang Chen,Qiyang Zhang,Guotao Quan,Min Ji,Chengjin Zhang,Yang Yongfeng,Xin Liu,Dong Liang,Hairong Zheng,Hu Zhanli +10 more
TL;DR: An algorithm based on a cycle-consistent generative adversarial network (CycleGAN) to suppress noise and reduce artifacts and includes attention mechanisms in the proposed network to expand the receptive field and capture richer contextual dependencies.
Journal ArticleDOI
Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey
TL;DR: In this article , a literature review on the application of GAN in ophthalmology image domains is presented to discuss important contributions and to identify potential future research directions, and a survey on studies using GAN published before June 2021 only is presented.
Journal ArticleDOI
Applications of artificial intelligence in dentistry: A comprehensive review
Francisco Carrillo-Perez,Oscar E. Pecho,Juan Carlos Morales,Rade D. Paravina,Alvaro Della Bona,Razvan Ghinea,Rosa Pulgar,María M. Pérez,Luis Javier Herrera +8 more
TL;DR: A comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
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