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dc.contributor.author Young, M.D.
dc.contributor.author Avena-Koenigsberger, A.
dc.contributor.author Hayashi, S.
dc.contributor.author Gopu, A.
dc.contributor.author West, J.
dc.contributor.author Paramasivam, M.
dc.contributor.author Perigo, R.
dc.date.accessioned 2019-07-31T17:08:18Z
dc.date.available 2019-07-31T17:08:18Z
dc.date.issued 2019
dc.identifier.uri http://hdl.handle.net/2022/23321
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 https://creativecommons.org/licenses/by/4.0/ 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


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