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

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.