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dc.contributor.author Gopu, A
dc.contributor.author Young, M.D.
dc.contributor.author West, J.
dc.contributor.author Parasivam, M.
dc.contributor.author Avena-Koenigsberger, A.
dc.date.accessioned 2019-07-31T17:08:46Z
dc.date.available 2019-07-31T17:08:46Z
dc.date.issued 2019
dc.identifier.uri http://hdl.handle.net/2022/23322
dc.description.abstract Medical imaging, a key component in the clinical diagnosis of and research on neurodegenerative diseases such as Alzheimer’s and Parkinson’s, and various brain cancers, can generate massive datasets in the order of tens to even hundreds of thousands of images for a single subject over the course of a study, and studies typically include hundreds of subjects. Quality assurance (QA) plays a critical role in guaranteeing high reliability of medical imaging research ; it requires continuous involvement by all stakeholders and use of specific quality control (QC) methods. Imaging is very costly, and many projects lack funds to reacquire images if QC issues are detected later, or this reacquisition may be impractical based on the distance between the patient and the facility, or difficulties inherent in the illness. Therefore, appropriate QC methods are imperative, as they can rapidly identify bad data and areas likely to require to post-processing correction or real-time re-acquisition. While many useful QC methods including automated or semi-automated procedures exist, they are often complex, limited in informational scope, require time consuming manual techniques, and lack documentation; they are generally designed for specific use-cases, making integration with other setups difficult. In this paper, we summarize the work done by the the Indiana University Scalable Compute Archive (IU SCA) team and the RADY Imaging Center (Indianapolis) between 2015-now on the Scalable Quality Assurance for Neuroimaging project (SQAN - pronounced “scan”) software suite. SQAN runs a comprehensive QC pipeline, comparing various facets of each scan for each subject with an appropriate template. It ensures all the scans required across each modality expected by a research study’s pre-defined protocol are present with the expected image counts; and that values match exactly, or within a percentage threshold of the template value. It safely ignores keywords expected to differ between the template and subject exam (e.g. timestamps, subject demographics). Outside of coding, our project required a significant amount of engagement and feedback cycles with researchers, scanner technologists, and data scientists, each of whom approach QC with their own unique priorities and insights. Since Fall 2017, a fully operational production SQAN service instance has supported the Indianapolis-based RADY Imaging Center (RADY-SQAN) providing QC for 50+ research projects; it has QC’ed nearly 3 million images and validated over 600 million metadata tags. A more detailed description of the QC capabilities is available in an accompanying technical description paper. 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): Quality Control for Medical Imaging Project en
dc.type Preprint en
dc.identifier.doi 10.5967/yk35-st11


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