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

TL;DR

This paper discusses the challenges of creating and utilizing large MRI datasets for research, proposing a quality assessment pipeline to address issues related to data heterogeneity, privacy, and standardization.

Contribution

It introduces a general design framework for a quality assessment pipeline to improve the usability of large-scale MRI data repositories.

Findings

Identified key challenges in large MRI data pooling and usage.

Proposed a quality assessment pipeline to enhance data reliability.

Outlined design principles for scalable MRI data quality evaluation.

Abstract

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.