ArticleIEEE transactions on medical imaging2022
Modeling Bias Error in 4D Flow MRI Velocity Measurements.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- Uncertainty Quantification in Hemodynamic Metrics from 4D Flow MRI with Super-resolution in a Carotid Bifurcation Model.Journal of imaging informatics in medicine · 2026Article
- Predicting Cerebral Aneurysm Rupture: The Role of Computational Fluid Dynamics.Neuroimaging clinics of North America · 2025Review
- Super-Resolving and Denoising 4D flow MRI of Neurofluids Using Physics-Guided Neural Networks.Annals of biomedical engineering · 2025Article
- 4D flow MRI velocity uncertainty quantification.Magnetic resonance in medicine · 2025Article
- Generalized Super-Resolution 4D Flow MRI - Using Ensemble Learning to Extend Across the Cardiovascular System.IEEE journal of biomedical and health informatics · 2024Article
- Transfer learning on physics-informed neural networks for tracking the hemodynamics in the evolving false lumen of dissected aorta.Nexus (New York, N.Y.) · 2024Article
- Enhancing the implantation of mechanical circulatory support devices using computational simulations.Frontiers in bioengineering and biotechnology · 2024Article
- Generalized super-resolution 4D Flow MRI-using ensemble learning to extend across the cardiovascular system.ArXiv · 2023Article
- Automatic 4D Flow MRI Segmentation Using the Standardized Difference of Means Velocity.IEEE transactions on medical imaging · 2023Article
- Flow-based method demonstrates improved accuracy for calculating wall shear stress in arterial flows from 4D flow MRI data.Journal of biomechanics · 2023Article
- Wall Shear Stress Estimation for 4D Flow MRI Using Navier-Stokes Equation Correction.Annals of biomedical engineering · 2022Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
We present a model to estimate the bias error of 4D flow magnetic resonance imaging (MRI) velocity measurements. The local instantaneous bias error is defined as the difference between the expectation of the voxel's measured velocity and actual velocity at the voxel center. The model accounts for bias error introduced by the intra-voxel velocity distribution and partial volume (PV) effects. We assess the intra-voxel velocity distribution using a 3D Taylor Series expansion. PV effects and numerical errors are considered using a Richardson extrapolation. The model is applied to synthetic Womersley flow and in vitro and in vivo 4D flow MRI measurements in a cerebral aneurysm. The bias error model is valid for measurements with at least 3.75 voxels across the vessel diameter and signal-to-noise ratio greater than 5. All test cases exceeded this diameter to voxel size ratio with diameters, isotropic voxel sizes, and velocity ranging from 3-15mm, 0.5-1mm, and 0-60cm/s, respectively. The model accurately estimates the bias error in voxels not affected by PV effects. In PV voxels, the bias error is an order of magnitude higher, and the accuracy of the bias error estimation in PV voxels ranges from 67.3% to 108% relative to the actual bias error. The bias error estimated for in vivo measurements increased two-fold at systole compared to diastole in partial volume and non-partial volume voxels, suggesting the bias error varies over the cardiac cycle. This bias error model quantifies 4D flow MRI measurement accuracy and can help plan 4D flow MRI scans.
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