ArticleMedical physics2020
Technical Note: MRQy - An open-source tool for quality control of MR imaging data.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed.
- Multi-center evaluation of radiomics and deep learning to stratify malignancy risk of IPMNs.Abdominal radiology (New York) · 2026Article
- Development of a Computed Tomography Enterography-Based Radiomics Model to Characterize Inflammation, Fibrosis, and Smooth Muscle Thickening in Stricturing Crohn's Disease.The American journal of gastroenterology · 2026Article
- Empirical evaluation of variability and multi-institutional generalizability of deep learning survival models: application to renal cancer CT scans.Computers in biology and medicine · 2026Article
- An Unsupervised Brain Extraction Quality Control Approach for Efficient Neuro-Oncology Studies.Journal of imaging informatics in medicine · 2026Article
- Assessing Impact of Data Quality in Early Post-Operative Glioblastoma Segmentation.Journal of imaging · 2026Article
- Predicting ADC map quality from T2-weighted MRI: A deep learning approach for early quality assessment to assist point-of-care.European journal of radiology · 2025Article
- Multi-modal and Multi-view Cervical Spondylosis Imaging Dataset.Scientific data · 2025Article
- Multi-center evaluation of radiomics and deep learning to stratify malignancy risk of IPMNs.Research square · 2025Article
- BEEx Is an Open-Source Tool That Evaluates Batch Effects in Medical Images to Enable Multicenter Studies.Cancer research · 2025Article
- Radiomics to Detect Inflammation and Fibrosis on Magnetic Resonance Enterography in Stricturing Crohn's Disease.Journal of Crohn's & colitis · 2024Article
- nnU-Net-based Segmentation of Tumor Subcompartments in Pediatric Medulloblastoma Using Multiparametric MRI: A Multi-institutional Study.Radiology. Artificial intelligence · 2024Article
- Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumors.Medical physics · 2024Article
- Image Quality Assessment Tool for Conventional and Dynamic Magnetic Resonance Imaging Acquisitions.Journal of imaging · 2024Article
- Large-scale uterine myoma MRI dataset covering all FIGO types with pixel-level annotations.Scientific data · 2024Article
- Automatic gross tumor volume segmentation with failure detection for safe implementation in locally advanced cervical cancer.Physics and imaging in radiation oncology · 2024Article
- Clinical efficacy of motion-insensitive imaging technique with deep learning reconstruction to improve image quality in cervical spine MR imaging.The British journal of radiology · 2024Article
- aXonica: A support package for MRI based Neuroimaging.Biotechnology notes (Amsterdam, Netherlands) · 2024Article
- CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models.Npj imaging · 2024Article
- Effects of MRI scanner manufacturers in classification tasks with deep learning models.Scientific reports · 2023Article
- DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeThere 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.
methodsUnlike 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.
resultsMRQy 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.
conclusionsMRQy 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.