MRQy: An Open-Source Tool for Quality Control of MR Imaging Data

04/10/2020 ∙ by Amir Reza Sadri, et al. ∙ 31

Even as public data repositories such as The Cancer Imaging Archive (TCIA) have enabled development of new radiomics and machine learning schemes, a key concern remains the generalizability of these methods to unseen datasets. For MRI datasets, model performance could be impacted by (a) site- or scanner-specific variations in image resolution, field-of-view, or image contrast, or (b) presence of imaging artifacts such as noise, motion, inhomogeneity, ringing, or aliasing; which can adversely affect relative image quality between data cohorts. This indicates a need for a quantitative tool to quickly determine relative differences in MRI volumes both within and between large data cohorts. We present MRQy, a new open-source quality control tool to (a) interrogate MRI cohorts for site- or equipment-based differences, and (b) quantify the impact of MRI artifacts on relative image quality; to help determine how to correct for these variations prior to model development. MRQy extracts a series of quality measures (e.g. noise ratios, variation metrics, entropy and energy criteria) and MR image metadata (e.g. voxel resolution, image dimensions) for subsequent interrogation via a specialized HTML5 based front-end designed for real-time filtering and trend visualization. MRQy is designed to be a standalone, unsupervised tool that can be efficiently run on a standard desktop computer. It has been made freely accessible at http://github.com/ccipd/MRQy for wider community use and feedback. MRQy was used to evaluate (a) n=133 brain MRIs from TCIA (7 sites), and (b) n=104 rectal MRIs (3 local sites). MRQy measures revealed significant site-specific variations in both cohorts, indicating potential batch effects. Marked differences in specific MRQy measures were also able to identify MRI datasets that needed to be corrected for common MR imaging artifacts.

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MRQy

MRQy is a quality assurance and checking tool for quantitative assessment of magnetic resonance imaging (MRI) data.


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