ArticleMagnetic resonance in medicine2023
4Dflow-VP-Net: A deep convolutional neural network for noninvasive estimation of relative pressures in stenotic flows from 4D flow MRI.
Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- Non-invasive quantification of pressure drops in stenotic intracranial vessels: using deep learning-enhanced 4D flow MRI to characterize the regional haemodynamics of the pulsing brain.Interface focus · 2025Article
- Three-Dimensionally Printed Elastic Cardiovascular Phantoms for Carotid Angioplasty Training and Personalized Healthcare.Journal of clinical medicine · 2024Article
- Prediction of Hemodynamic-Related Hemolysis in Carotid Stenosis and Aiding in Treatment Planning and Risk Stratification Using Computational Fluid Dynamics.Biomedicines · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
purposeTo estimate relative transvalvular pressure gradient (TVPG) noninvasively from 4D flow MRI.
methodsA novel deep learning-based approach is proposed to estimate pressure gradient across stenosis from four-dimensional flow MRI (4D flow MRI) velocities. A deep neural network 4D flow Velocity-to-Presure Network (4Dflow-VP-Net) was trained to learn the spatiotemporal relationship between velocities and pressure in stenotic vessels. Training data were simulated by computational fluid dynamics (CFD) for different pulsatile flow conditions under an aortic flow waveform. The network was tested to predict pressure from CFD-simulated velocity data, in vitro 4D flow MRI data, and in vivo 4D flow MRI data of patients with both moderate and severe aortic stenosis. TVPG derived from 4Dflow-VP-Net was compared to catheter-based pressure measurements for available flow rates, in vitro and Doppler echocardiography-based pressure measurement, in vivo.
resultsRelative pressures calculated by 4Dflow-VP-Net and in vitro pressure catheterization revealed strong correlation (r
conclusionThe proposed approach can predict relative pressure in both in vitro and in vivo 4D flow MRI of aortic stenotic patients with high fidelity.
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Registered trials
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