ArticleIEEE transactions on medical imaging2021
Divergence-Free Constrained Phase Unwrapping and Denoising for 4D Flow MRI Using Weighted Least-Squares.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- 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
- An On-Site InSAR Terrain Imaging Method with Unmanned Aerial Vehicles.Sensors (Basel, Switzerland) · 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
- Accelerated dual-venc 4D flow MRI with variable high-venc spatial resolution for neurovascular applications.Magnetic resonance in medicine · 2022Article
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Authors and funding
9 authors.
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Abstract
A novel divergence-free constrained phase unwrapping method was proposed and evaluated for 4D flow MRI. The unwrapped phase field was obtained by integrating the phase variations estimated from the wrapped phase data using weighted least-squares. The divergence-free constraint for incompressible blood flow was incorporated to regulate and denoise the resulting phase field. The proposed method was tested on synthetic phase data of left ventricular flow and in vitro 4D flow measurement of Poiseuille flow. The method was additionally applied to in vivo 4D flow measurements in the thoracic aorta from 30 human subjects. The performance of the proposed method was compared to the state-of-the-art 4D single-step Laplacian algorithm. The synthetic phase data were completely unwrapped by the proposed method for all the cases with velocity encoding (venc) as low as 20% of the maximum velocity and signal-to-noise ratio as low as 5. The in vitro Poiseuille flow data were completely unwrapped with a 60% increase in the velocity-to-noise ratio. For the in-vivo aortic datasets with venc ratio less than 0.4, the proposed method significantly improved the success rate by as much as 40% and reduced the velocity error levels by a factor of 10 compared to the state-of-the-art method. The divergence-free constrained method exhibits reliability and robustness on phase unwrapping and shows improved accuracy of velocity and hemodynamic quantities by unwrapping the low-venc 4D flow MRI data.
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