Engineering AI Tools for Systematic and Scalable Quality Assessment in Magnetic Resonance Imaging
This addresses data quality problems for researchers and clinicians in medical imaging, but it is incremental as it builds on existing quality assessment concepts.
The paper tackles the challenges of constructing and using large MRI data repositories due to privacy, format, and heterogeneity issues, proposing a quality assessment pipeline as a solution.
A desire to achieve large medical imaging datasets keeps increasing as machine learning algorithms, parallel computing, and hardware technology 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 a frequently used, non-invasive imaging modality. However, constructing a big MRI data repository has multiple challenges related to privacy, data size, DICOM format, logistics, and non-standardized images. Not only building 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 imaging sites. This position paper describes challenges in constructing a large MRI data repository and using data downloaded from such data repositories in various aspects. To help address the challenges, the paper proposes introducing a quality assessment pipeline, with considerations and general design principles.