Technical Note: MRQy - An open-source tool for quality control of MR imaging data.

Details

Serval ID
serval:BIB_01DE4E083575
Type
Article: article from journal or magazin.
Collection
Publications
Title
Technical Note: MRQy - An open-source tool for quality control of MR imaging data.
Journal
Medical physics
Author(s)
Sadri A.R., Janowczyk A., Zhou R., Verma R., Beig N., Antunes J., Madabhushi A., Tiwari P., Viswanath S.E.
ISSN
2473-4209 (Electronic)
ISSN-L
0094-2405
Publication state
Published
Issued date
12/2020
Peer-reviewed
Oui
Volume
47
Number
12
Pages
6029-6038
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
There is an increasing availability of large imaging cohorts [such as through The Cancer Imaging Archive (TCIA)] for computational model development and imaging research. To ensure development of generalizable computerized models, there is a need to quickly determine relative quality differences in these cohorts, especially when considering MRI datasets which can exhibit wide variations in image appearance. The purpose of this study is to present a quantitative quality control tool, MRQy, to help interrogate MR imaging datasets for: (a) site- or scanner-specific variations in image resolution or image contrast, and (b) imaging artifacts such as noise or inhomogeneity; which need correction prior to model development.
Unlike existing imaging quality control tools, MRQy has been generalized to work with images from any body region to efficiently extract a series of quality measures (e.g., noise ratios, variation metrics) and MR image metadata (e.g., voxel resolution and image dimensions). MRQy also offers a specialized HTML5-based front-end designed for real-time filtering and trend visualization of quality measures.
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. Before processing, MRQy measures could be used to identify each of the seven sites within the TCIA cohort with 87.5%, 86.4%, 90%, 93%, 90%, 60%, and 92.9% accuracy and the three sites within the rectal cohort with 91%, 82.8%, and 88.9% accuracy using unsupervised clustering. After processing, none of the sites could be distinctively clustered via MRQy measures in either cohort; suggesting that batch effects had been largely accounted for. Marked differences in specific MRQy measures were also able to identify outlier MRI datasets that needed to be corrected for common acquisition 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 and open-source at http://github.com/ccipd/MRQy for community use and feedback.
Keywords
Artifacts, Cohort Studies, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Quality Control, MRI, acquisition artifacts, batch effects, imaging variations, noise, quality control
Pubmed
Web of science
Create date
16/11/2020 14:29
Last modification date
12/10/2021 16:01
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