Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19

Wu, Weiwen; Yu, Hengyong; Wu, Weifei; Vardhanabhuti, Varut

doi:10.1109/tim.2021.3050190

Cited by 9 publications

(7 citation statements)

References 47 publications

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“…A straightforward solution is to perform the denoising process on the sinogram data before image reconstruction, i.e., sinogram filtration-based methods [20]- [22]. Iterative reconstruction methods combine the statistics of raw data in the sinogram domain [23], [24] and the prior information in the image domain, such as total variation [25] and dictionary learning [26]; these pieces of generic information can be effectively integrated into the maximum likelihood and compressed sensing frameworks. These two categories, however, require the access to raw data that are typically unavailable from commercial CT scanner.…”

Section: A Ldct Denoisingmentioning

confidence: 99%

DU-GAN: Generative Adversarial Networks With Dual-Domain U-Net-Based Discriminators for Low-Dose CT Denoising

Huang

Zhang

et al. 2022

IEEE Trans. Instrum. Meas.

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Low-dose computed tomography (LDCT) has drawn major attention in the medical imaging field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing the radiation dose, however, decreases the quality of the reconstructed images, which consequently compromises the diagnostic performance. Over the past few years, various deep learning techniques, especially generative adversarial networks (GANs), have been introduced to improve the image quality of LDCT images through denoising, achieving impressive results over traditional approaches. GAN-based denoising methods usually leverage an additional classification network, i.e., discriminator, to learn the most discriminate difference between the denoised and normal-dose images and, hence, regularize the denoising model accordingly; it often focuses either on the global structure or local details. To better regularize the LDCT denoising model, this article proposes a novel method, termed DU-GAN, which leverages U-Net-based discriminators in the GAN framework to learn both global and local differences between the denoised and normal-dose images in both image and gradient domains. The merit of such a U-Net-based discriminator is that it can not only provide the per-pixel feedback to the denoising network through the outputs of the U-Net but also focus on the global structure in a semantic level through the middle layer of the U-Net. In addition to the adversarial training in the image domain, we also apply another U-Net-based discriminator in the image gradient domain to alleviate the artifacts caused by photon starvation and enhance the edge of the denoised CT

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Section: A Ldct Denoisingmentioning

confidence: 99%

DU-GAN: Generative Adversarial Networks With Dual-Domain U-Net-Based Discriminators for Low-Dose CT Denoising

Huang

Zhang

et al. 2022

IEEE Trans. Instrum. Meas.

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show abstract

“…sinogram filtration-based methods [19], [20], [21]. Iterative reconstruction methods combine the statistics of raw data in the sinogram domain [22], [23] and the prior information in the image domain such as total variation [24] and dictionary learning [25]; these pieces of generic information can be effectively integrated in the maximum likelihood and compressed sensing frameworks. These two categories, however, require the access to raw data that are typically unavailable from commercial CT scanner.…”

Section: A Ldct Denoisingmentioning

confidence: 99%

DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising

Huang,

Zhang,

Zhang

et al. 2021

Preprint

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Low-dose computed tomography (LDCT) has drawn major attention in the medical imaging field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing the radiation dose, however, decreases the quality of the reconstructed images, which consequently compromises the diagnostic performance. Over the past few years, various deep learning techniques, especially generative adversarial networks (GANs), have been introduced to improve the image quality of LDCT images through denoising, achieving impressive results over traditional approaches. GANs-based denoising methods usually leverage an additional classification network, i.e. discriminator, to learn the most discriminate difference between the denoised and normal-dose images and, hence, regularize the denoising model accordingly; it often focuses either on the global structure or local details. To better regularize the LDCT denoising model, this paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images in both image and gradient domains. The merit of such a U-Net based discriminator is that it can not only provide the per-pixel feedback to the denoising network through the outputs of the U-Net but also focus on the global structure in a semantic level through the middle layer of the U-Net. In addition to the adversarial training in the image domain, we also apply another U-Net based discriminator in the image gradient domain to alleviate the artifacts caused by photon starvation and enhance the edge of the denoised CT images. Furthermore, the CutMix technique enables the per-pixel outputs of the U-Net based discriminator to provide radiologists with a confidence map to visualize the uncertainty of the denoised results, facilitating the LDCT-based screening and diagnosis. Extensive experiments on the simulated and real-world datasets demonstrate superior performance over recently published methods both qualitatively and quantitatively. Our source code is made available at https://github.com/Hzzone/DU-GAN.

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“…Efforts devoted to mitigating the effects of COVID-19 transmission have been conducted since its appearance in December 2019. Recently, there have been many studies conducted to understand and manage the COVID-19 pandemic by developing several techniques for different applications, such as wearing mask detection 20 , COVID-19 spread forecasting 21 , and chest X-rays diagnosis 22 . Wearable technologies have been recently demonstrated promising solutions to aid in mitigating infectious diseases, such as COVID-19.…”

Section: Introductionmentioning

confidence: 99%

A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models

Alali

Harrou

Sun

2022

Sci Rep

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This study aims to develop an assumption-free data-driven model to accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the Gaussian process regression (GPR) hyperparameters to develop an efficient GPR-based model for forecasting the recovered and confirmed COVID-19 cases in two highly impacted countries, India and Brazil. However, machine learning models do not consider the time dependency in the COVID-19 data series. Here, dynamic information has been taken into account to alleviate this limitation by introducing lagged measurements in constructing the investigated machine learning models. Additionally, we assessed the contribution of the incorporated features to the COVID-19 prediction using the Random Forest algorithm. Results reveal that significant improvement can be obtained using the proposed dynamic machine learning models. In addition, the results highlighted the superior performance of the dynamic GPR compared to the other models (i.e., Support vector regression, Boosted trees, Bagged trees, Decision tree, Random Forest, and XGBoost) by achieving an averaged mean absolute percentage error of around 0.1%. Finally, we provided the confidence level of the predicted results based on the dynamic GPR model and showed that the predictions are within the 95% confidence interval. This study presents a promising shallow and simple approach for predicting COVID-19 spread.

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Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19

Cited by 9 publications

References 47 publications

DU-GAN: Generative Adversarial Networks With Dual-Domain U-Net-Based Discriminators for Low-Dose CT Denoising

DU-GAN: Generative Adversarial Networks With Dual-Domain U-Net-Based Discriminators for Low-Dose CT Denoising

DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising

A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models

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