ArticleScientific reports2022
Synthesis of patient-specific multipoint 4D flow MRI data of turbulent aortic flow downstream of stenotic valves.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Quantitative Evaluation of Carotid Artery Stenosis by Multi-VENC 4D Flow MRI: Incorporating Turbulent Kinetic Energy for Clinical Validity.Journal of magnetic resonance imaging : JMRI · 2025Article
- Intracranial and extracranial artery stenosis and clinical outcome of acute ischemic stroke patients receiving intravenous thrombolysis.Frontiers in neurology · 2025Article
- Generalized Super-Resolution 4D Flow MRI - Using Ensemble Learning to Extend Across the Cardiovascular System.IEEE journal of biomedical and health informatics · 2024Article
- Optimizing encoding strategies for 4D Flow MRI of mean and turbulent flow.Scientific reports · 2024Article
- 4D Flow cardiovascular magnetic resonance consensus statement: 2023 update.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Review
- Advances in machine learning applications for cardiovascular 4D flow MRI.Frontiers in cardiovascular medicine · 2022Review
- Synthetically trained convolutional neural networks for time-resolved aortic segmentation of four-dimensional flow magnetic resonance imaging.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
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4 authors.
Funding
Abstract
We propose to synthesize patient-specific 4D flow MRI datasets of turbulent flow paired with ground truth flow data to support training of inference methods. Turbulent blood flow is computed based on the Navier-Stokes equations with moving domains using realistic boundary conditions for aortic shapes, wall displacements and inlet velocities obtained from patient data. From the simulated flow, synthetic multipoint 4D flow MRI data is generated with user-defined spatiotemporal resolutions and reconstructed with a Bayesian approach to compute time-varying velocity and turbulence maps. For MRI data synthesis, a fixed hypothetical scan time budget is assumed and accordingly, changes to spatial resolution and time averaging result in corresponding scaling of signal-to-noise ratios (SNR). In this work, we focused on aortic stenotic flow and quantification of turbulent kinetic energy (TKE). Our results show that for spatial resolutions of 1.5 and 2.5 mm and time averaging of 5 ms as encountered in 4D flow MRI in practice, peak total turbulent kinetic energy downstream of a 50, 75 and 90% stenosis is overestimated by as much as 23, 15 and 14% (1.5 mm) and 38, 24 and 23% (2.5 mm), demonstrating the importance of paired ground truth and 4D flow MRI data for assessing accuracy and precision of turbulent flow inference using 4D flow MRI exams.
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