The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve COVID-19 treatments by providing imaging information to assess the severity and progression of the disease. Chest computed tomography (CT) has been shown to be sensitive to COVID-19 via the observation of ground-glass opacities and is being used on patients with acute symptoms. Practical considerations such as high radiation to the patient and cross contamination risk from moving the patient for imaging must be taken into account in the decision to image with CT. A 3D imaging solution that is low cost, low radiation, and mobile could provide advantages and bring quality of care while integrating efficiently in the hospital workflow. Along with addressing the current crisis, the usage of this solution to address other respiratory diseases would secure strong commercial potential for this research. This Small Business Innovation Research (SBIR) Phase I project seeks to develop and validate an artificial intelligence (AI)-enabled 3D imaging reconstruction algorithm that can be used to assess the severity and progression of respiratory diseases such as COVID-19. Current chest imaging technologies can either provide adequate image quality or efficient imaging of the lungs, but not both. Two major advances could make the imaging more efficient while also providing the required image quality. Scatter modeling has been shown to be successful in improving image quality when reconstructing from few radiographs; Preliminary data shows how Machine Learning (ML) can be integrated to enhance efficient imaging to provide higher quality images. A 3D image creation algorithm that models X-ray scatter and uses ML to reconstruct 3D images from rapid radiographs will enable using 3D imaging for respiratory diseases including COVID-19. This algorithm will be validated on cadaveric models to assess if an AI-enabled imaging system that is mobile, that can be used bedside, and that is easily draped for sterile utilization is feasible. 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.
