Show simple item record Young, M.D. Avena-Koenigsberger, A. Hayashi, S. Gopu, A. West, J. Paramasivam, M. Perigo, R. 2019-07-31T17:08:18Z 2019-07-31T17:08:18Z 2019
dc.description.abstract In medical research a single magnetic resonance imaging (MRI) exam of a single subject can produce hundreds of thousands of individual images and millions of key-value metadata pairs which must be verified to ensure instrument performance and compliance with the research protocol. Here we describe a system to address this concern, the Scalable Quality Assurance for Neuroimaging (SQAN), an open-source suite of tools used to extract metadata and perform quality control (QC) protocol and instrumental validation on medical imaging files (e.g. DICOM). The design features several discrete components, including: systems for receiving and storing incoming live data from remote imaging centers; processes for performing quality control validation on new and archive data; an Application Programming Interface (API) for mediating secure authorized access to imaging data and QC results; and a web-based User Interface (UI) for viewing stored data, QC results, modifying QC templates and access controls, commenting on QC issues, and alerting affected researchers, and re-running QC tests as needed. This paper is the second in a series, with the first discussing the background, motivations, and broad overview of SQAN as a project. In this paper we will provide a low-level technical description of the systems, methods, and infrastructure of the SQAN application stack. In addition to a further examination of the principal SQAN components we will explore additional features, including: anonymization of electronically Protected Health Information (ePHI); secure data transfer from remote imaging centers; extraction and compression of imaging metadata; optimized mongo database structure; and the QC templates and validations, including exclusions and handling of edge-cases, which are numerous. We will also describe the lifecycle of typical medical imaging exam, from acquisition through QC acceptance. en
dc.language.iso en en
dc.rights.uri en
dc.title Scalable Quality Assurance for Neuroimaging (SQAN) - Technical design including software application stack en
dc.type Preprint en
dc.identifier.doi 10.5967/481s-nt96

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search IUScholarWorks

Advanced Search


My Account