ArticleArtificial intelligence in medicine2020
Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.
Article in Artificial intelligence in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Promises and challenges of AI-enabled methods for myocardial characterisation in cardiovascular magnetic resonance.Frontiers in cardiovascular medicine · 2026Review
- Advancements in High-Resolution Computed Tomography: Revolutionising Bone Health Micro-Research.Bioengineering (Basel, Switzerland) · 2025Article
- Deep Convolutional Neural Networks Provide Motion Grading for High-Resolution Peripheral Quantitative Computed Tomography of the Scaphoid.Diagnostics (Basel, Switzerland) · 2024Article
- The influence of left bundle branch block on myocardial T1 mapping.Scientific reports · 2024Article
- Swin Transformer and the Unet Architecture to Correct Motion Artifacts in Magnetic Resonance Image Reconstruction.International journal of biomedical imaging · 2024Article
- Imaging for the diagnosis of acute myocarditis: can artificial intelligence improve diagnostic performance?Frontiers in cardiovascular medicine · 2024Review
- Validation of a deep learning-based software for automated analysis of T2 mapping in cardiac magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2023Article
- Magnetic resonance myocardial T1ρ mapping : Technical overview, challenges, emerging developments, and clinical applications.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Review
- Cardiovascular magnetic resonance for evaluation of cardiac involvement in COVID-19: recommendations by the Society for Cardiovascular Magnetic Resonance.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Review
- Model-free phasor image analysis of quantitative myocardial TScientific reports · 2022Article
- Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.Circulation · 2022Article
- Accelerated cardiac TJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022Article
- Article
- Cardiac stress T1-mapping response and extracellular volume stability of MOLLI-based T1-mapping methods.Scientific reports · 2021Article
- MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks.Frontiers in cardiovascular medicine · 2021Article
- Stress CMR in Known or Suspected CAD: Diagnostic and Prognostic Role.BioMed research international · 2021Review
Corrections and comments
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Authors and funding
9 authors.
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Abstract
Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tissue characterisation. However, cardiac or respiratory motion can significantly affect the diagnostic utility of T1-maps, and thus motion artefact detection is critical for quality control and clinically-robust T1 measurements. Manual quality control of T1-maps may provide reassurance, but is laborious and prone to error. We present a deep learning approach with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping. Firstly, we customised a multi-stream Convolutional Neural Network (CNN) image classifier to streamline the process of automatic motion artefact detection. Secondly, we imposed attention supervision to guide the CNN to focus on targeted myocardial segments. Thirdly, when there was disagreement between the human operator and machine, a second human validator reviewed and rescored the cases for adjudication and to identify the source of disagreement. The multi-stream neural networks demonstrated 89.8% agreement, 87.4% ROC-AUC on motion artefact detection with the human operator in the 2568 T1 maps. Trained with additional supervision on attention, agreements and AUC significantly improved to 91.5% and 89.1%, respectively (p < 0.001). Rescoring of disagreed cases by the second human validator revealed that human operator error was the primary cause of disagreement. Deep learning with attention supervision provides a quick and high-quality assurance of clinical images, and outperforms human operators.
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