The NIH is increasing its investment in large, multi-center brain MRI studies through projects such as the BRAIN initiative. The success of these studies depends on the quality of MRIs and the resulting image measurements. Kitware’s team with collaborators from academia is addressing this need by developing software that will help assess the images’ quality using deep learning methods.
Many neuroscience studies rely on in-house procedures that combine automatically generated scores with manually guided checks. This is often done by combining several software systems that are not designed to support Quality Assurance (QA) or Quality Control (QC) processes. Furthermore, collaborative technologies that assist a distributed team in implementing rapid and more accurate Quality Control activities are missing in multi-site QC workflows. Therefore, we are developing the open-source Medical Image Quality Assurance (MIQA) software. MIQA represents a design that facilitates collaboration and sharing. It also incorporates a state-of-the-art deep learning system to help focus reviewers’ attention on potentially problematic scans.
- Simplifies the creation of custom QC workflows in compliance with study requirements
- Provides core functionality for performing QC of medical images
- Automatically generate documentation compliant with the FAIR principle (making scientific results Findable, Accessible, Interoperable, and Reusable)
We designed MIQA for medical imaging QA/QC from the ground up. Users will be able to configure workflows that reflect the specific requirements of medical imaging studies. We also aim to minimize the time spent on labor-intensive operations, such as visually reviewing scans. Our deep learning techniques will include an incremental learning strategy that continuously improves the machine-generated QC score.
The usefulness of this unique QC system will be tested through user studies such as the multi-center National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study . and The Institute for Clinical and Translational Science, University of Iowa.
An in-progress version of MIQA has been deployed on the NCANDA server and is used daily by the NCANDA team to receive feedback from our clinical collaborators at this early stage of development. Current developments are focused on user interface reorganization. The UI now includes a display of AI-produced scan ratings. The next step is the addition of multi-user roles and permissions.
This project is funded by NIH grant R44 MH119022 from the National Institute of Mental Health.