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

Award ID: 2036377

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.

NSF Program Director: Alastair Monk