RAPID: A Computational Deep-Learning Approach for Fast, Accurate CT Testing and Monitoring of COVID-19

RAPID:一种计算深度学习方法,可快速、准确地进行 CT 测试和 COVID-19 监测

基本信息

项目摘要

The coronavirus disease COVID-19 is now a global pandemic, causing a huge health and economic crisis at an unprecedented scale. Despite new testing modalities, there remains an urgent need for fast, accurate, and accessible tools to test people of suspected COVID-19 and monitor disease progression. The ComputeCOVID19+ project addresses this need by providing a computationally-based screening tool that delivers much higher accuracy for screening and monitoring than current laboratory-based technique (i.e., PCR). The ComputeCOVID19+ system will also make analysis of Computerized Tomography (CT) scans faster to reduce the burden on radiologists and healthcare systems. The ComputeCOVID19+ project addresses the challenges of COVID screening and monitoring in (1) reconstructing super-resolution medical images from conventional CT scanners, (2) developing novel algorithms and software for high-fidelity image reconstruction and high-precision interpretation of COVID-19, and (3) validating our approach with clinical COVID-19 data. The method uses CT scans and the team’s super-resolution and deblur-based iterative reconstruction (SADIR) algorithm. As a result, the SADIR-based neural network has better explanation and robustness. In addition, it involves a much smaller number of training parameters, and hence, is easier to train. Finally, SADIR does not require any high-resolution CT images as the “ground truth” reference during network training. The expected outcome is a computational deep learning method that can detect and diagnose COVID-19 with high sensitivity and high specificity. The method will also enable monitoring of COVID-19 disease progression with better accuracy.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.
新型冠状病毒病COVID-19现已成为全球性大流行病,导致前所未有的巨大健康和经济危机。尽管有新的检测模式,但仍然迫切需要快速,准确和可访问的工具来检测疑似COVID-19的人并监测疾病进展。ComputeCOVID 19+项目通过提供基于计算的筛查工具来满足这一需求,该工具可以比当前基于实验室的技术提供更高的筛查和监测准确性(即,PCR)。ComputeCOVID 19+系统还将更快地分析计算机断层扫描(CT)扫描,以减轻放射科医生和医疗保健系统的负担。ComputeCOVID 19+项目旨在解决COVID筛查和监测的挑战,包括(1)从传统CT扫描仪重建超分辨率医学图像,(2)开发用于高保真图像重建和高精度COVID-19解读的新型算法和软件,以及(3)使用临床COVID-19数据验证我们的方法。该方法使用CT扫描和该团队的超分辨率和基于去模糊的迭代重建(SADIR)算法。结果表明,基于SADIR的神经网络具有更好的解释性和鲁棒性。此外,它涉及的训练参数数量要少得多,因此更容易训练。最后,SANDON不需要任何高分辨率CT图像作为网络训练期间的“地面实况”参考。预期的结果是一种计算深度学习方法,可以以高灵敏度和高特异性检测和诊断COVID-19。该方法还将能够更准确地监测COVID-19疾病进展。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。

项目成果

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Wuchun Feng其他文献

Wuchun Feng的其他文献

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{{ truncateString('Wuchun Feng', 18)}}的其他基金

Collaborative Research: Workshop Series on Sustainable Computing
协作研究:可持续计算研讨会系列
  • 批准号:
    2125999
  • 财政年份:
    2021
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
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
  • 财政年份:
    2020
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
Phase-I IUCRC Virginia Tech: Center for Space, High-performance, and Resilient Computing (SHREC)
第一阶段 IUCRC 弗吉尼亚理工大学:空间、高性能和弹性计算中心 (SHREC)
  • 批准号:
    1822080
  • 财政年份:
    2018
  • 资助金额:
    $ 20万
  • 项目类别:
    Continuing Grant
NSF XPS Workshop for Exploiting Parallelism and Scalability
NSF XPS 利用并行性和可扩展性研讨会
  • 批准号:
    1451021
  • 财政年份:
    2014
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
EAGER: Collaborative Research: Democratizing the Teaching of Parallel Computing Concepts
EAGER:协作研究:并行计算概念教学的民主化
  • 批准号:
    1353786
  • 财政年份:
    2013
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
BIGDATA: Mid-Scale: DA: Collaborative Research: Genomes Galore - Core Techniques, Libraries, and Domain Specific Languages for High-Throughput DNA Sequencing
大数据:中规模:DA:协作研究:基因组丰富 - 高通量 DNA 测序的核心技术、库和领域特定语言
  • 批准号:
    1247693
  • 财政年份:
    2013
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
XPS: SDA: Collaborative Research: A Scalable and Distributed System Framework for Compute-Intensive and Data-Parallel Applications
XPS:SDA:协作研究:用于计算密集型和数据并行应用的可扩展分布式系统框架
  • 批准号:
    1337131
  • 财政年份:
    2013
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
CiC (RDDC): Commoditizing Data-Intensive Biocomputing in the Cloud
CiC (RDDC):云中数据密集型生物计算的商品化
  • 批准号:
    1048253
  • 财政年份:
    2011
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
MRI-R2: Acquisition of a Heterogeneous Supercomputing Instrument for Transformative Interdisciplinary Research
MRI-R2:获取用于变革性跨学科研究的异构超级计算仪器
  • 批准号:
    0960081
  • 财政年份:
    2010
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
CSR: Small: Collaborative Research: Hybrid Opportunistic Computing for Green Clouds
CSR:小型:协作研究:绿色云的混合机会计算
  • 批准号:
    0916719
  • 财政年份:
    2009
  • 资助金额:
    $ 20万
  • 项目类别:
    Continuing Grant

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  • 批准号:
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  • 批准年份:
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  • 项目类别:
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