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

这个小型企业创新研究(SBIR)第一阶段项目的更广泛的影响/商业潜力是改善医疗成像设备的放射学和管理。建议的分析平台将提供对设备利用率的详细洞察，以帮助监督运营、优化工作流程、更好地利用现有设备并评估投资的成功。更有效地使用扫描仪预计将极大地造福于患者群体，因为这将减少磁共振成像(MRI)的等待时间，增加患者探视，缩短成像方案，减少镇静时间，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辟新的研究途径。这个小型企业创新研究(SBIR)第一阶段项目旨在开发一种技术，重新调整磁共振成像(MRI)扫描仪创建的数字成像和通信(DICOM)数据的用途，以建立一个统一的、可查询的成像检查知识来源。该项目将协调DICOM元数据，并在其基础上创建描述成像检查所有方面的本体。开发领域包括通过分析图像和检查来推断扫描仪何时真正处于活动状态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来自动检测重复图像，从而展示了数据源的影响，重复图像是进度延误的一个突出来源。总体而言，该项目的发展从DICOM数据构建了时间和工作流程的关键方面，以实现放射学中新形式的数据分析。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.