Scalable Quality Assurance for Neuroimaging (SQAN): Automated quality control for medical imaging

摘要

Medical imaging, a key component in clinical diagnosis of and research on numerous medical conditions, is verycostly and can generate massive datasets. For instance, a single scanned subject produces hundreds of thousandsof images and millions of key-value metadata pairs that must be veri ed to ensure instrument and researchprotocol compliance. Many projects lack funds to reacquire images if data quality issues are detected later. Dataquality assurance (QA) requires continuous involvement by all stakeholders and use of specific quality control(QC) methods to identify data issues likely to require post-processing correction or real-time re-acquisition.While many useful QC methods exist, they are often designed for specific use-cases with limited scope anddocumentation, making integration with other setups di cult. We present the Scalable Quality Assurancefor Neuroimaging (SQAN), an open-source software suite developed by Indiana University for protocol qualitycontrol and instrumental validation on medical imaging data. SQAN includes a comprehensive QC Enginethat ensures adherence to a research study's protocol. A modern, intuitive web portal serves a wide range ofusers including researchers, scanner technologists and data scientists, each of whom approach QC with uniquepriorities, expertise, insights and expectations. Since Fall 2017, a fully operational SQAN instance has supported50+ research projects, and has QC'd ～3.5 million images and over 700 million metadata tags. SQAN is designedto scale to any imaging center's QC needs, and to extend beyond protocol QC toward image-level QC andintegration with pipeline and non-imaging database systems.