Scalable Quality Assurance for Neuroimaging (SQAN) - Technical design including software application stack

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.