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

新冠肺炎大流行在美国引发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，需要高精度和可获得的检测技术，以便通过广泛部署来缓解COVID-19疫情。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），其中某些材料在美国不同地区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可广泛使用的替代COVID-19测试方法，并在几分钟内以高精度提供结果。通过实现、部署和不断改进一种高性能软件工具，通过对计算机断层扫描（CT）扫描进行图像后增强和分析，促进对COVID-19的早期和准确检测和监测，该研究将利用计算机处理的许多x射线测量组合来生成胸部（特别是肺部）的横截面图像，从而促进实时准确的COVID-19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，创建了一个高性能软件工具，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、双侧和周围疾病、线状混浊、“疯狂铺路”模式和“反向光环”征象。具体而言，我们实现了一种新的深度学习神经网络，该网络可以提高胸部CT图像的分辨率并减少伪影。它通过模拟胸部CT的图像形成过程，为CT图像提供一个超分辨率和去模糊的迭代框架。神经网络只学习CT超分辨率任务最优解中相关的模糊核、合适的权重因子和正则化项的惩罚函数。总而言之，这一有利方法将为快速诊断和监测COVID-19提供一种高度准确和高度可用的检测方法，从而减轻COVID-19对公共卫生、社会和经济的负面影响。该奖项反映了美国国家科学基金会的法定使命，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.