RAPID: Higher Accuracy and Availability of COVID-19 Testing and Monitoring via Post-CT Image Boosting and Analysis

COVID-19疫情在美国造成了前所未有的健康危机。鉴于短期内缺乏有效的疫苗或药物，需要高准确性和可用性的测试技术，以通过广泛部署来缓解COVID-19的爆发。然而，目前基于基因的COVID-19检测涉及许多不同的材料（例如，拭子、试管和化学溶液），其中某些在美国各地的不同时间供应短缺。此外，测试是一个多步骤的过程，容易出错，导致准确性低。为了解决这些缺点，该项目寻求提供一种替代的COVID-19测试，该测试可以广泛使用，并在几分钟内提供高准确性的结果。通过实现、部署和不断改进高性能软件工具，以促进通过计算机断层扫描（CT）扫描的图像后增强和分析对COVID-19进行早期和准确的测试和监测，该研究将促进真实的COVID-19的准确诊断。该项目利用并扩展了人工智能和高性能计算的最新进展，以创建一个高性能的软件工具，以显着提高胸部CT图像的质量。这些增强的CT图像反过来有助于更准确地分析和识别COVID-19的标志性特征，包括实变、双侧和外周疾病、线性混浊、“疯狂铺路”模式和“反向光晕”征。具体来说，我们实现了一种新型的深度学习神经网络，可以提高分辨率并减少胸部CT图像的伪影。它通过对胸部CT中的图像形成过程进行建模，为CT图像提供超分辨率和基于去模糊的迭代框架。神经网络仅学习CT超分辨率任务的最优解中的相关模糊核、适当的加权因子和正则化项的惩罚函数。总而言之，这种支持方法将通过为COVID-19的快速诊断和监测提供高度准确和高度可用的测试来减轻COVID-19对公共卫生、社会和经济的负面影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The COVID-19 pandemic has caused an unprecedented health crisis in the United States. Given the lack of an effective vaccine or drug in the short term, testing techniques with high accuracy and availability are needed to mitigate the COVID-19 outbreak through expansive deployment. However, the current genetic-based test for COVID-19 involves many different materials (e.g., swabs, tubes, and chemical solutions), of which certain ones are in short supply at different times in different places across the United States. Furthermore, the test is a multi-step process that is error-prone, resulting in low accuracy. To address these shortcomings, this project seeks to deliver an alternative COVID-19 test that can be widely available and deliver results in minutes with high accuracy. By realizing, deploying, and continually improving a high-performance software tool to facilitate early and accurate testing and monitoring of COVID-19 via post-image boosting and analysis of computed tomography (CT) scans, which use computer-processed combinations of many X-ray measurements to produce cross-section images of the chest (in particular, the lungs) this research will facilitate accurate COVID-19 diagnosis in real time. The project leverages and extends recent advances in artificial intelligence and high-performance computing to create a high-performance software tool to significantly enhance the quality of chest CT images. These enhanced CT images, in turn, facilitate more accurate analysis and identification of the hallmark features of COVID-19, including consolidation, bilateral and peripheral disease, linear opacities, “crazy-paving” patterns, and the “reverse halo” sign. Specifically, we realize a novel deep-learning neural network that enhances the resolution and reduces the artifacts of chest CT images. It does so by modeling the image-formation processes in chest CT to deliver a super-resolution and deblur-based iterative framework for CT images. The neural network only learns the relevant blur kernels, appropriate weighting factors, and penalty functions of the regularization terms in the optimal solution for the CT super-resolution task. All told, this enabling approach will mitigate the negative effects of COVID-19 on public health, society, and the economy by delivering a highly accurate and highly available test for the rapid diagnosis and monitoring of COVID-19.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.