Scalable Quality Assurance for Neuroimaging (SQAN): automated quality control for medical imaging. Gopu, A., Young, M., D., Avena-Koenigsberger, A., Perigo, R., W., West, J., D., Paramasivam, M., Hayashi, S., & Henschel, R. In Deserno, T., M. & Chen, P., editors, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, volume 11318, pages 6, 3, 2020. SPIE. ![Scalable Quality Assurance for Neuroimaging (SQAN): automated quality control for medical imaging [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 1 download Medical imaging, a key component in clinical diagnosis of and research on numerous medical conditions, is very costly and can generate massive datasets. For instance, a single scanned subject produces hundreds of thousands of images and millions of key-value metadata pairs that must be verified to ensure instrument and research protocol compliance. Many projects lack funds to reacquire images if data quality issues are detected later. Data quality 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 and documentation, making integration with other setups difficult. We present the Scalable Quality Assurance for Neuroimaging (SQAN), an open-source software suite developed by Indiana University for protocol quality control and instrumental validation on medical imaging data. SQAN includes a comprehensive QC Engine that ensures adherence to a research study’s protocol. A modern, intuitive web portal serves a wide range of users including researchers, scanner technologists and data scientists, each of whom approach QC with unique priorities, expertise, insights and expectations. Since Fall 2017, a fully operational SQAN instance has supported 50+ research projects, and has QC’d ∼3.5 million images and over 700 million metadata tags. SQAN is designed to scale to any imaging center’s QC needs, and to extend beyond protocol QC toward image-level QC and integration with pipeline and non-imaging database systems.
@inproceedings{
title = {Scalable Quality Assurance for Neuroimaging (SQAN): automated quality control for medical imaging},
type = {inproceedings},
year = {2020},
keywords = {angularjs,automated quality control,javascript portal,medical research imaging,mongodb,node.js,protocol compliance,vue.js},
pages = {6},
volume = {11318},
websites = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11318/2549722/Scalable-Quality-Assurance-for-Neuroimaging-SQAN--automated-quality-control/10.1117/12.2549722.full},
month = {3},
publisher = {SPIE},
day = {2},
id = {e2e6a75f-b533-3b18-bf22-931dd37c1e31},
created = {2020-04-23T05:12:40.694Z},
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abstract = {Medical imaging, a key component in clinical diagnosis of and research on numerous medical conditions, is very costly and can generate massive datasets. For instance, a single scanned subject produces hundreds of thousands of images and millions of key-value metadata pairs that must be verified to ensure instrument and research protocol compliance. Many projects lack funds to reacquire images if data quality issues are detected later. Data quality 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 and documentation, making integration with other setups difficult. We present the Scalable Quality Assurance for Neuroimaging (SQAN), an open-source software suite developed by Indiana University for protocol quality control and instrumental validation on medical imaging data. SQAN includes a comprehensive QC Engine that ensures adherence to a research study’s protocol. A modern, intuitive web portal serves a wide range of users including researchers, scanner technologists and data scientists, each of whom approach QC with unique priorities, expertise, insights and expectations. Since Fall 2017, a fully operational SQAN instance has supported 50+ research projects, and has QC’d ∼3.5 million images and over 700 million metadata tags. SQAN is designed to scale to any imaging center’s QC needs, and to extend beyond protocol QC toward image-level QC and integration with pipeline and non-imaging database systems.},
bibtype = {inproceedings},
author = {Gopu, Arvind and Young, Michael D. and Avena-Koenigsberger, Andrea and Perigo, Raymond W. and West, John D. and Paramasivam, Meenakshisundaram and Hayashi, Soichi and Henschel, Robert},
editor = {Deserno, Thomas M. and Chen, Po-Hao},
doi = {10.1117/12.2549722},
booktitle = {Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications}
}
Downloads: 1
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