ArticleRadiology. Artificial intelligence2021
Landmark Detection in Cardiac MRI by Using a Convolutional Neural Network.
Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Automated vs manual cardiac MRI planning: a single-center prospective evaluation of reliability and scan times.European radiology · 2025Trial
- Liraglutide Improves Myocardial Perfusion and Energetics and Exercise Tolerance in Patients With Type 2 Diabetes.Journal of the American College of Cardiology · 2024Trial
- Automated Cardiac Reorientation and Slice Extraction in Cine Coronary CT Angiography: Agreement with Cardiovascular Magnetic Resonance.Tomography (Ann Arbor, Mich.) · 2026Article
- Real-Time, Inline Quantitative MRI Enabled by Scanner-Integrated Machine Learning: A Proof of Principle With NODDI.Magnetic resonance in medicine · 2026Article
- The role of artificial intelligence in pediatric cardiovascular imaging: clinical applications and future directions in computed tomography and magnetic resonance imaging.Pediatric radiology · 2026Review
- Fully automated free-breathing cardiac magnetic resonance imaging at 3T: A prospective randomized study of image quality, efficiency, and workflow burden.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2026Article
- Development and validation of a versatile foundation model for cine cardiac magnetic resonance image analysis.Communications medicine · 2026Article
- A computer-aided method for the automatic localization of the left ventricular long axis on coronary computed tomography angiography.Quantitative imaging in medicine and surgery · 2026Article
- Revolutionizing Heart Failure Management With Artificial Intelligence: A Narrative Review of Diagnostic, Prognostic, and Therapeutic Innovations.Health science reports · 2026Article
- Quantifying 3D foot and ankle alignment using an AI-driven framework: a pilot study.Skeletal radiology · 2026Article
- Deep learning for cardiac MRI: performance evidence and barriers to clinical integration. A Systematic Review and Meta-Analysis.European heart journal. Imaging methods and practice · 2026Article
- AI-powered SPOT imaging for enhanced myocardial scar detection and quantification.Nature communications · 2025Article
- Automated Field of View Prescription for Whole-body Magnetic Resonance Imaging Using Deep Learning Based Body Region Segmentations.Investigative radiology · 2025Article
- Shaping the future of cardiac interventions and cardiac surgeries: The impact of virtual reality and artificial intelligence.Global cardiology science & practice · 2025Review
- Advanced Myocardial MRI Tissue Characterization Combining Contrast Agent-Free T1-Rho Mapping With Fully Automated Analysis.Journal of magnetic resonance imaging : JMRI · 2025Article
- Automatic flow planning for fetal cardiovascular magnetic resonance imaging.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- HusMorph: a simple machine learning app for automated morphometric landmarking.Conservation physiology · 2025Article
- Towards a cardiovascular magnetic resonance foundation model for multi-task cardiac image analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance.Reviews in cardiovascular medicine · 2024Review
- Left Ventricular Hypertrophy in Aortic Stenosis: Early Cell and Matrix Regression 2 Months Post-Aortic Valve Replacement.Circulation. Cardiovascular imaging · 2024Observational
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeTo develop a convolutional neural network (CNN) solution for landmark detection in cardiac MRI (CMR). MATERIALS AND
methodsThis retrospective study included cine, late gadolinium enhancement (LGE), and T1 mapping examinations from two hospitals. The training set included 2329 patients (34 089 images; mean age, 54.1 years; 1471 men; December 2017 to March 2020). A hold-out test set included 531 patients (7723 images; mean age, 51.5 years; 323 men; May 2020 to July 2020). CNN models were developed to detect two mitral valve plane and apical points on long-axis images. On short-axis images, anterior and posterior right ventricular (RV) insertion points and left ventricular (LV) center points were detected. Model outputs were compared with manual labels assigned by two readers. The trained model was deployed to MRI scanners.
resultsFor the long-axis images, successful detection of cardiac landmarks ranged from 99.7% to 100% for cine images and from 99.2% to 99.5% for LGE images. For the short-axis images, detection rates were 96.6% for cine, 97.6% for LGE, and 98.7% for T1 mapping. The Euclidean distances between model-assigned and manually assigned labels ranged from 2 to 3.5 mm for different landmarks, indicating close agreement between model-derived landmarks and manually assigned labels. For all views and imaging sequences, no differences between the models' assessment of images and the readers' assessment of images were found for the anterior RV insertion angle or LV length. Model inference for a typical cardiac cine series took 610 msec with the graphics processing unit and 5.6 seconds with central processing unit.
conclusionA CNN was developed for landmark detection on both long- and short-axis CMR images acquired with cine, LGE, and T1 mapping sequences, and the accuracy of the CNN was comparable with the interreader variation.
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