ABSTRACT
The pandemic of coronavirus disease 2019 (COVID-19) has threatened the social and economic structure all around the world. Generally, COVID-19 has three possible transmission routes, including pre-symptomatic, symptomatic and asymptomatic transmission, among which the last one has brought a severe challenge for the containment of the disease. One core scientific question is to understand the influence of asymptomatic individuals and of the strength of control measures on the evolution of the disease, particularly on a second outbreak of the disease. To explore these issues, we proposed a novel compartmental model that takes the infection of asymptomatic individuals into account. We get the relationship between asymptomatic individuals and critical strength of control measures theoretically. Furthermore, we verify the reliability of our model and the accuracy of the theoretical analysis by using the real confirmed cases of COVID-19 contamination. Our results, showing the importance of the asymptomatic population on the control measures, would provide useful theoretical reference to the policymakers and fuel future studies of COVID-19.
ABSTRACT
Background：Dental departments generally employ cone-beam computed tomography (CBCT) instead of conventional computed tomography (CT), due to its lower price, smaller dosage, and high spatial resolution. During the corona virus disease 2019 (COVID-19) outbreak, CBCT is highly recommended to replace intraoral radiography because it greatly reduces the risk of exposure to salivary droplets. However, CBCT's inability to quantitatively measure tissue attenuation limits its application in differential diagnosis. Methods:We employed a U-Net based network to generate synthetic CT from dental CBCT. The deep neural network can be trained end-to-end to learn the complex mapping between CBCT and CT values. By the U-Net architecture, low-level and high-level features are both utilized to get fine detailed synthetic CT. We applied our method on the collected dataset contains 62 patients. Results:Experimental results on four metrics -- mean absolute error (MAE), root-mean-square error (RMSE), structural similarity index (SSIM), and peak-signal-to-noise ratio (PSNR) -- showed significant improvement of the synthetic CT compared to the original CBCT data. The MAE and RMSE improvement percentages are 64.44% and 66.44%.The MAE level of synthetic CT for most of the tissues are small enough to separate most important tissues,including dentin and cancellous bone, dentin and root canal，implants and cortical bone.Conclusions:CBCT and synthetic CT values can be used to distinguish different high-attenuation structures that are of interest to dentists. The application of CBCT assisted by this U-net based network in medical imaging of other parts of the body is promising.