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

RAPID：通过 CT 后图像增强和分析提高 COVID-19 测试和监测的准确性和可用性

• 批准号: 2031215
• 负责人: Wuchun Feng
• 金额: $ 15万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031215&HistoricalAwards=false
• 关键词: RAPID; Higher; Accuracy; Availability; COVID

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.

新冠肺炎疫情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，需要高准确性和可用性的检测技术，通过大规模部署来缓解新冠肺炎疫情。然而，目前基于基因的新冠肺炎检测涉及许多不同的材料(如棉签、试管和化学溶液)，其中某些材料在美国各地不同时间出现短缺。此外，测试是一个多步骤的过程，容易出错，导致准确率较低。为了克服这些缺点，该项目寻求提供一种替代的新冠肺炎测试，该测试可以广泛使用，并在几分钟内以高精度提供结果。通过实现、部署和不断改进一个高性能的软件工具，通过图像后增强和计算机断层扫描(CT)扫描的分析，促进对新冠肺炎的早期和准确的测试和监测，这些扫描使用计算机处理的许多X射线测量的组合来产生胸部(尤其是肺部)的横断面图像，这项研究将有助于实时准确的新冠肺炎诊断。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，以显著提高胸部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新冠肺炎的标志性特征，包括实变、双侧和周围疾病、线状浑浊、“疯狂铺路”图案和“反晕”征。具体地说，我们实现了一种新型的深度学习神经网络，它提高了胸部CT图像的分辨率，减少了伪影。它通过模拟胸部CT的成像过程来实现这一点，从而为CT图像提供超分辨率和基于去模糊的迭代框架。神经网络只学习CT超分辨率任务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，这种使能的方法将通过提供高精度和高可用性的检测来快速诊断和监测新冠病毒19，从而缓解新冠肺炎对公共卫生、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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

ComputeCOVID19+: Accelerating COVID-19 Diagnosis and Monitoring via High-Performance Deep Learning on CT Images

• DOI: 10.1145/3472456.3473523
• 发表时间: 2021-08
• 期刊: Proceedings of the 50th International Conference on Parallel Processing
• 作者: Garvit Goel;Atharva Gondhalekar;Jingyuan Qi;Zhicheng Zhang;Guohua Cao;Wuna Feng