ArticleRadiology. Artificial intelligence2019
Implementation and Validation of a Three-dimensional Cardiac Motion Estimation Network.
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 18 papers.
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Who cites it
18 citing papers in PubMed, 51 citations in OpenAlex.
- A deep-learning-based pipeline for automatic fusion of CT coronary angiogram and stress perfusion CMR.Medical physics · 2026Article
- Efficient cosine-windowed cross-correlation for intermediate deformable image registration.Scientific reports · 2026Article
- Multi-Frame Image Registration for Automated Ventricular Function Assessment in Single Breath-Hold Cine MRI Using Limited Labels.Magnetic resonance in medicine · 2026Article
- Navigator motion-resolved MR fingerprinting using implicit neural representation: Feasibility for free-breathing three-dimensional whole-liver multiparametric mapping.Magnetic resonance in medicine · 2026Article
- An unsupervised method for MRI recovery: deep image prior with structured sparsity.Magma (New York, N.Y.) · 2025Article
- Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction.ArXiv · 2025Article
- A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.Medical image analysis · 2025Review
- Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance.Reviews in cardiovascular medicine · 2024Review
- Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.Magma (New York, N.Y.) · 2024Review
- Stop moving: MR motion correction as an opportunity for artificial intelligence.Magma (New York, N.Y.) · 2024Review
- MRXCAT2.0: Synthesis of realistic numerical phantoms by combining left-ventricular shape learning, biophysical simulations and tissue texture generation.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- Intermediate Deformable Image Registration via Windowed Cross-Correlation.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- DeepStrain Evidence of Asymptomatic Left Ventricular Diastolic and Systolic Dysfunction in Young Adults With Cardiac Risk Factors.Frontiers in cardiovascular medicine · 2022Article
- Real-time 3D motion estimation from undersampled MRI using multi-resolution neural networks.Medical physics · 2021Article
- Multi-Atlas Image Soft Segmentation via Computation of the Expected Label Value.IEEE transactions on medical imaging · 2021Article
- DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics.Frontiers in cardiovascular medicine · 2021Article
- A Convolutional Neural Network-based Deformable Image Registration Method for Cardiac Motion Estimation from Cine Cardiac MR Images.Computing in cardiology · 2020Article
- Machine Learning Approaches for Myocardial Motion and Deformation Analysis.Frontiers in cardiovascular medicine · 2019Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo describe an unsupervised three-dimensional cardiac motion estimation network (CarMEN) for deformable motion estimation from two-dimensional cine MR images. MATERIALS AND
methodsA function was implemented using CarMEN, a convolutional neural network that takes two three-dimensional input volumes and outputs a motion field. A smoothness constraint was imposed on the field by regularizing the Frobenius norm of its Jacobian matrix. CarMEN was trained and tested with data from 150 cardiac patients who underwent MRI examinations and was validated on synthetic (
resultsOn the synthetic dataset, CarMEN achieved a median DSC of 0.85, which was higher than all five methods (minimum-maximum median [or MMM], 0.67-0.84;
conclusionThe proposed deep learning-based approach for three-dimensional cardiac motion estimation allowed the derivation of a motion model that balances motion characterization and image registration accuracy and achieved motion estimation accuracy comparable to or better than that of several state-of-the-art image registration algorithms.© 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.