ArticleJournal of magnetic resonance imaging : JMRI2019
Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks.
Article in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- A systematic review of (semi-)automatic quality control of T1-weighted MRI scans.Neuroradiology · 2024Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Time Series Segmentation in Adaptive Optics Ophthalmoscopy With Application to Flicker-Induced Vasodilation.Translational vision science & technology · 2026Article
- Artificial intelligence in medicine: Current applications in cardiology, oncology, and radiology.World journal of methodology · 2025Review
- Deep learning-based no-reference quality assessment of anterior segment ultrasound biomicroscopy panoramic images.Quantitative imaging in medicine and surgery · 2025Article
- BrainQCNet: A Deep Learning attention-based model for the automated detection of artifacts in brain structural MRI scans.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Quantifying MR head motion in the Rhineland Study - A robust method for population cohorts.NeuroImage · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Can we predict motion artifacts in clinical MRI before the scan completes?Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition · 2023Article
- Pre-training via Transfer Learning and Pretext Learning a Convolutional Neural Network for Automated Assessments of Clinical PET Image Quality.IEEE transactions on radiation and plasma medical sciences · 2023Article
- Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms.European radiology · 2022Article
- Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.Frontiers in molecular neuroscience · 2022Review
- Estimating subjective evaluation of low-contrast resolution using convolutional neural networks.Physical and engineering sciences in medicine · 2021Article
- Deep learning enabled brain shunt valve identification using mobile phones.Computer methods and programs in biomedicine · 2021Article
- Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review).Experimental and therapeutic medicine · 2021Review
- Image Quality Assessment of Fetal Brain MRI Using Multi-Instance Deep Learning Methods.Journal of magnetic resonance imaging : JMRI · 2021Article
- Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images.Diagnostics (Basel, Switzerland) · 2021Review
- Recommendation for Cardiac Magnetic Resonance Imaging-Based Phenotypic Study: Imaging Part.Phenomics (Cham, Switzerland) · 2021Review
- Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundDeep learning (DL) is a promising methodology for automatic detection of abnormalities in brain MRI. PURPOSE: To automatically evaluate the quality of multicenter structural brain MRI images using an ensemble DL model based on deep convolutional neural networks (DCNNs). STUDY TYPE: Retrospective. POPULATION: The study included 1064 brain images of autism patients and healthy controls from the Autism Brain Imaging Data Exchange (ABIDE) database. MRI data from 110 multiple sclerosis patients from the CombiRx study were included for independent testing. SEQUENCE: T ASSESSMENT: The ABIDE data were separated into training (60%), validation (20%), and testing (20%) sets. The ensemble DL model combined the results from three cascaded networks trained separately on the three MRI image planes (axial, coronal, and sagittal). Each cascaded network consists of a DCNN followed by a fully connected network. The quality of image slices from each plane was evaluated by the DCNN and the resultant image scores were combined into a volumewise quality rating using the fully connected network. The DL predicted ratings were compared with manual quality evaluation by two experts. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve, area under ROC curve (AUC), sensitivity, specificity, accuracy, and positive (PPV) and negative (NPV) predictive values.
resultsThe AUC, sensitivity, specificity, accuracy, PPV, and NPV for image quality evaluation of the ABIDE test set using the ensemble model were 0.90, 0.77, 0.85, 0.84, 0.42, and 0.96, respectively. On the CombiRx set the same model achieved performance of 0.71, 0.41, 0.84, 0.73, 0.48, and 0.80. DATA
conclusionThis study demonstrated the high accuracy of DL in evaluating image quality of structural brain MRI in multicenter studies. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1260-1267.
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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.