Operational Ontology for Radiology: A Framework for Profiling and Characterizing CT Imaging Data.
Authors
Affiliations (6)
Affiliations (6)
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC, USA. [email protected].
- Department of Radiology, Duke University Medical Center, Durham, NC, USA. [email protected].
- Clinical Imaging Physics Group, Duke University Health System, Durham, NC, USA. [email protected].
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC, USA.
- Department of Radiology, Duke University Medical Center, Durham, NC, USA.
- Clinical Imaging Physics Group, Duke University Health System, Durham, NC, USA.
Abstract
Medical imaging data are increasingly used for benchmarking, quality improvement, research, and artificial intelligence applications. However, the lack of consistent metadata characterization limits the reliability and scalability of such analyses. Existing standards provide syntactic interoperability but often fail to capture the semantic clinical context and workflow details necessary for secondary uses such as AI development and radiation safety assessment, affecting the utility for reproducible profiling of imaging examinations across institutions. In this work, we describe key gaps in current approaches to imaging data standardization. These include syntactic interoperability without semantic equivalence, limited representation of clinically relevant context, inconsistent definitions of radiology procedures across institutions, and the absence of mechanisms to systematically profile imaging examinations for analysis. These limitations make it difficult to compare imaging practices, aggregate datasets, and interpret results across sites. To address these challenges, we have devised an ontology framework, the Operational Ontology for Radiology (OOR), that models the radiology study workflow from ordering through interpretation using explicitly defined entities, relationships, and annotated data properties. A CT-focused implementation of the framework was then formed, including 34 classes, 15 object properties, and 156 data properties in this present formation. The framework was deployed in a demonstrative use case scenario to illustrate the utility of OOR in drawing more clinically relevant radiation dose aggregates from clinical CT data. The work demonstrates that OOR provides both a structured representation of imaging data and a foundation for more transparent data aggregation, scalable secondary analysis, and reproducible characterization of imaging practices across institutions.