Engineering AI Tools for Systematic and Scalable Quality Assessment in Magnetic Resonance Imaging

A desire to achieve a large medical imaging dataset keeps increasing as machine learning algorithms, parallel computing, and hardware evolve. Accordingly, there is a growing demand in pooling data from multiple clinical and academic institutes to enable large-scale clinical or translational research studies. Magnetic resonance imaging (MRI) is one of the most frequently used imaging technique that is not invasive. However, constructing a big MRI data repository has multiple challenges such as privacy issues, image size, and issues with DICOM. Not only constructing the data repository is difficult, but using data pooled from the repository is also challenging, due to heterogeneity in image acquisition, reconstruction, and processing pipelines across MRI vendors and sites. This position paper describes challenges of constructing a large MRI data repository and using data downloaded from such data repository in various aspects. Furthermore, the paper proposes introducing a QA pipeline that can help addressing the challenges described and provides general considerations and design principles.