  We sought to develop a quantitative tool to quickly determine relative
differences in MRI volumes both within and between large MR imaging cohorts
(such as available in The Cancer Imaging Archive (TCIA)), in order to help
determine the generalizability of radiomics and machine learning schemes to
unseen datasets. The tool is intended to help quantify presence of (a) site- or
scanner-specific variations in image resolution, field-of-view, or image
contrast, or (b) imaging artifacts such as noise, motion, inhomogeneity,
ringing, or aliasing; which can adversely affect relative image quality between
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 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 outlier MRI
datasets that needed to be corrected for common MR imaging artifacts. 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
\url{http://github.com/ccipd/MRQy} for wider community use and feedback.
