SBIR Phase I: Unified data description layer for magnetic resonance imaging scanners

SBIR 第一阶段：磁共振成像扫描仪的统一数据描述层

• 批准号: 2036377
• 负责人: Benoit Scherrer
• 金额: $ 25.55万
• 依托单位: QUANTIVLY, 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: 2021-02-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036377&HistoricalAwards=false
• 关键词: SBIR; Phase; Unified; data; description

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve radiology and management of medical imaging equipment. The proposed analytics platform will provide detailed insight into device utilization to help oversee operations, optimize workflows, better leverage existing equipment, and evaluate the success of investments. More efficient use of scanners is expected to substantially benefit the patient population as it will reduce the wait time for magnetic resonance imaging (MRI), increase patient access, shorten imaging protocols, reduce sedation duration, reduce and predict delays, and ultimately improve the patient experience. The data unlocked by the platform will also open new avenues of research for radiologists and researchers.This Small Business Innovation Research (SBIR) Phase I project aims to develop a technology that repurposes the Digital Imaging and Communications in Medicine (DICOM) data created by magnetic resonance imaging (MRI) scanners to build a unified, query-able source of knowledge about imaging exams. This project will harmonize DICOM metadata and build upon it to create an ontology that describes all the facets of imaging exams. Areas of development include recovering acquisition duration and scanner activity through algorithms that analyze images and exams to infer when the scanner was truly active. The project also demonstrates the impact of the data source by training a machine learning model to automatically detect repeated images, a prominent source of schedule delays. Overall, the developments from this project construct key aspects of timing and workflow from DICOM data to enable a new form of data analytics in Radiology.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.

这项小型企业创新研究（SBIR）I期项目的更广泛的影响/商业潜力是改善医学成像设备的放射学和管理。拟议的分析平台将对设备利用率提供详细的见解，以帮助监督操作，优化工作流程，更好地利用现有设备并评估投资的成功。 预计扫描仪的更有效利用将大大受益于患者人群，因为它将减少磁共振成像（MRI）的等待时间，增加患者的访问权限，缩短成像协议，减少镇静持续时间，减少和预测延迟，并最终改善患者体验。该平台解锁的数据还将为放射科医生和研究人员开放新的研究途径。这项小型企业创新研究（SBIR）I阶段项目旨在开发一项技术，将磁共振成像（MRI）扫描仪（MRI）扫描仪创建的数字成像和通信（DICOM）数据重新利用，以构建一个独立的知识来源，以构建有关知识的知识。该项目将协调DICOM元数据并在其基础上建立一个本体，以描述成像考试的所有方面。发展领域包括通过分析扫描仪真正活跃的算法来恢复采集持续时间和扫描仪活动。该项目还通过训练机器学习模型自动检测重复的图像，这是数据源的影响，这是进度延迟的突出来源。 总体而言，该项目的发展构造了时间和工作流程的关键方面，从DICOM数据启用了放射学中的新形式的数据分析形式。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准通过评估来获得支持的。