ArticleRadiology. Artificial intelligence2019
Deep Learning-based Prescription of Cardiac MRI Planes.
Article in Radiology. Artificial intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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The trial behind it
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Who cites it
38 citing papers in PubMed.
- Automated vs manual cardiac MRI planning: a single-center prospective evaluation of reliability and scan times.European radiology · 2025Trial
- Automated Cardiac Reorientation and Slice Extraction in Cine Coronary CT Angiography: Agreement with Cardiovascular Magnetic Resonance.Tomography (Ann Arbor, Mich.) · 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
- Reproducibility of cardiac volumetric parameters derived from fully automatically prescribed image planes: a direct comparison to manual planning at 1.5-T and 3-T MRI.European radiology · 2026Article
- 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
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- Deep learning-based automatic field of view planning for prostate MRI in oblique coronal and oblique axial planes.Scientific reports · 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
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Fully Automated Plane Prescription in Cardiac MRI: A Prospective Cohort Study.Journal of magnetic resonance imaging : JMRI · 2026Article
- Deep Learning Automated Measurement of Shunt Severity with Estimation of Uncertainty in 4D Flow MRI.Radiology. Cardiothoracic imaging · 2026Article
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- Applying artificial intelligence to cardiac MRI to diagnose congenital heart disease in low-resource settings such as Sub-Saharan Africa.Communications medicine · 2025Review
- Automated Field of View Prescription for Whole-body Magnetic Resonance Imaging Using Deep Learning Based Body Region Segmentations.Investigative radiology · 2025Article
- Multi-Model Segmentation Algorithm for Rotator Cuff Injury Based on MRI Images.Bioengineering (Basel, Switzerland) · 2025Article
- Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow EfficiencyCurrent medical imaging · 2025Review
- Improving privacy-preserving multi-faceted long short-term memory for accurate evaluation of encrypted time-series MRI images in heart disease.Scientific reports · 2024Article
- Deep learning-based automated scan plane positioning for brain magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2024Article
- Review
- New clinical opportunities of low-field MRI: heart, lung, body, and musculoskeletal.Magma (New York, N.Y.) · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeTo develop and evaluate a system to prescribe imaging planes for cardiac MRI based on deep learning (DL)-based localization of key anatomic landmarks. MATERIALS AND
methodsAnnotated landmarks on 892 long-axis (LAX) and 493 short-axis (SAX) cine steady-state free precession series from cardiac MR images were retrospectively collected between February 2012 and June 2017. U-Net-based heatmap regression was used for localization of cardiac landmarks, which were used to compute cardiac MRI planes. Performance was evaluated by comparing localization distances and plane angle differences between DL predictions and ground truth. The plane angulations from DL were compared with those prescribed by the technologist at the original time of acquisition. Data were split into 80% for training and 20% for testing, and results confirmed with fivefold cross-validation.
resultsOn LAX images, DL localized the apex within mean 12.56 mm ± 19.11 (standard deviation) and the mitral valve (MV) within 7.68 mm ± 6.91. On SAX images, DL localized the aortic valve within 5.78 mm ± 5.68, MV within 5.90 mm ± 5.24, pulmonary valve within 6.55 mm ± 6.39, and tricuspid valve within 6.39 mm ± 5.89. On the basis of these localizations, average angle bias and mean error of DL-predicted imaging planes relative to ground truth annotations were as follows: SAX, -1.27° ± 6.81 and 4.93° ± 4.86; four chambers, 0.38° ± 6.45 and 5.16° ± 3.80; three chambers, 0.13° ± 12.70 and 9.02° ± 8.83; and two chamber, 0.25° ± 9.08 and 6.53° ± 6.28, respectively.
conclusionDL-based anatomic localization is a feasible strategy for planning cardiac MRI planes. This approach can produce imaging planes comparable to those defined by ground truth landmarks.© RSNA, 2019
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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.