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

arXiv: https://arxiv.org/abs/2004.04871 · HTML: https://mlsanity.com/paper/2004.04871/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging](https://mlsanity.com/paper/2109.09658/) (2021)
- [Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse](https://mlsanity.com/paper/2104.08131/) (2021)
- [A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation](https://mlsanity.com/paper/2109.02413/) (2021)
- [An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training](https://mlsanity.com/paper/2206.03359/) (2022)
- [Metadata-Aligned 3D MRI Representations for Contrast Understanding and Quality Control](https://mlsanity.com/paper/2511.00681/) (2025)
- [Continuous longitudinal fetus brain atlas construction via implicit neural representation](https://mlsanity.com/paper/2209.06413/) (2022)
- [SF2Former: Amyotrophic Lateral Sclerosis Identification From Multi-center MRI Data Using Spatial and Frequency Fusion Transformer](https://mlsanity.com/paper/2302.10859/) (2023)
- [Cyst-X: A Multi-Center MRI Benchmark and Federated Learning Framework for Malignancy-Risk Stratification of Pancreatic Cystic Neoplasm](https://mlsanity.com/paper/2507.22017/) (2025)
- [Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy](https://mlsanity.com/paper/2111.01093/) (2021)
- [CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models](https://mlsanity.com/paper/2307.08673/) (2023)
- [RAISE -- Radiology AI Safety, an End-to-end lifecycle approach](https://mlsanity.com/paper/2311.14570/) (2023)
- [MMRQA: Signal-Enhanced Multimodal Large Language Models for MRI Quality Assessment](https://mlsanity.com/paper/2509.24888/) (2025)

## Abstract (arXiv)

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 urlhttp://github.com/ccipd/MRQy for wider community use and feedback.