SBIR Phase I: Artificial Intelligence (AI) Enabled 3-Dimensional (3D) Imaging for COVID-19

SBIR 第一阶段：针对 COVID-19 的人工智能 (AI) 3 维 (3D) 成像

• 批准号: 2036690
• 负责人: Cristian Atria
• 金额: $ 25.6万
• 依托单位: nView medical Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036690&HistoricalAwards=false
• 关键词: SBIR; Phase; Artificial; Intelligence; AI

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.

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是通过提供成像信息来评估疾病的严重程度和进展，从而改善COVID-19的治疗方法。胸部计算机断层扫描（CT）通过观察毛玻璃混浊被证明对COVID-19很敏感，正在用于急性症状患者。在决定使用CT成像时，必须考虑到对患者的高辐射和移动患者进行成像的交叉污染风险等实际因素。低成本、低辐射和可移动的3D成像解决方案可以提供优势并带来高质量的护理，同时有效地集成到医院工作流程中。在解决当前危机的同时，使用这种解决方案来解决其他呼吸系统疾病将确保这项研究具有强大的商业潜力。这项小企业创新研究（SBIR）第一阶段项目旨在开发和验证一种支持人工智能（AI）的3D成像重建算法，该算法可用于评估COVID-19等呼吸系统疾病的严重程度和进展。目前的胸部成像技术要么提供足够的图像质量，要么提供有效的肺部成像，但不能两者兼而有之。两个主要的进步可以使成像更有效，同时也提供所需的图像质量。散射建模已被证明是成功的提高图像质量时，从少数射线照片重建；初步数据显示了如何集成机器学习（ML）来增强高效成像以提供更高质量的图像。一种3D图像创建算法可以模拟x射线散射，并使用ML从快速x光片中重建3D图像，这将使包括COVID-19在内的呼吸系统疾病能够使用3D成像。该算法将在尸体模型上进行验证，以评估一种可移动的、可在床边使用的、易于无菌使用的人工智能成像系统是否可行。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。