Doctors and patients alike rely on medical imaging – Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) – for accurate diagnosis and treatment of a variety of conditions. These technologies are particularly important for patients who have, and for researchers who investigate, neurodegenerative conditions like Alzheimer’s and Parkinson’s.
These scans generate massive datasets – tens to even hundreds of thousands of images – for each subject, and in a research context, studies often include hundreds of subjects, which makes quality assurance (QA) an important, if daunting, part of the medical imaging process. Importantly, QA requires the use of appropriate quality control (QC) methods in order to identify – ideally, “in the moment” – data in need of post-processing correction or reacquisition. Imaging is costly, and many projects (and patients!) lack the funds to reacquire images if Quality Control issues are detected after the acquisition period.
Within the research context, statistical inaccuracies from incorrect imaging parameters, low image quality, scanner software updates, and motion artifacts can create noise in data, leading to unreliable and irreducible results. And, because imaging data is often made available to the general scientific community, it can also impact future research.
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. Automated and flexible QC on imaging protocols supplement existing imaging QA/QC methods, and are particularly critical for multi-center projects with heterogeneous data. Over the past 4 years, the Scalable Compute Archive (SCA) team at Indiana University developed Scalable Quality Assurance for Neuroimaging (SQAN - pronounced “scan”), an open-source software suite for protocol quality control and instrumental validation on medical imaging data.
SQAN runs a comprehensive QC pipeline, ensuring adherence to a research study’s protocol, and includes a modern web portal user interface. The project involved significant engagement with researchers, scanner technologists, and data scientists, each of whom approach QC with their own unique priorities, expertise, insights, and web portal expectations. Since Fall 2017, a fully operational production SQAN service instance has supported 50+ research projects and has QC’d roughly 3 million images and over 600 million metadata tags.
A demo instance of SQAN can be viewed athttps://sqan.sca.iu.edu. If you are interested in using SQAN for your medical imaging protocol QC needs, please contact Arvind Gopu <email@example.com>.