ArticleMedical image analysis2021
Using synthetic data generation to train a cardiac motion tag tracking neural network.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Few-shot learning for highly accelerated 3D time-of-flight MRA reconstruction.Magnetic resonance in medicine · 2026Article
- Feasibility of tagged MRI at 0.55 T.Magnetic resonance in medicine · 2026Article
- Characterizing variability in passive myocardial stiffness in healthy human left ventricles using personalized MRI and finite element modeling.Scientific reports · 2025Article
- Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week.Journal of the American College of Cardiology · 2024Review
- DeepMesh: Mesh-Based Cardiac Motion Tracking Using Deep Learning.IEEE transactions on medical imaging · 2024Article
- SpasticSim: a synthetic data generation method for upper limb spasticity modelling in neurorehabilitation.Scientific reports · 2024Article
- ECG-based estimation of respiration-induced autonomic modulation of AV nodal conduction during atrial fibrillation.Frontiers in physiology · 2024Article
- Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- 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
- Cardiac magnetic resonance radiomics for disease classification.European radiology · 2023Article
- Synthesizing Complex-Valued Multicoil MRI Data from Magnitude-Only Images.Bioengineering (Basel, Switzerland) · 2023Article
- Myocardial Segmentation of Tagged Magnetic Resonance Images with Transfer Learning Using Generative Cine-To-Tagged Dataset Transformation.Bioengineering (Basel, Switzerland) · 2023Article
- Native-resolution myocardial principal Eulerian strain mapping using convolutional neural networks and Tagged Magnetic Resonance Imaging.Computers in biology and medicine · 2022Article
- Quantification of exercised-induced myocardial deformation with exercise CMR.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
A CNN based method for cardiac MRI tag tracking was developed and validated. A synthetic data simulator was created to generate large amounts of training data using natural images, a Bloch equation simulation, a broad range of tissue properties, and programmed ground-truth motion. The method was validated using both an analytical deforming cardiac phantom and in vivo data with manually tracked reference motion paths. In the analytical phantom, error was investigated relative to SNR, and accurate results were seen for SNR>10 (displacement error <0.3 mm). Excellent agreement was seen in vivo for tag locations (mean displacement difference = -0.02 pixels, 95% CI [-0.73, 0.69]) and calculated cardiac circumferential strain (mean difference = 0.006, 95% CI [-0.012, 0.024]). Automated tag tracking with a CNN trained on synthetic data is both accurate and precise.
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Registered trials
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