ArticleMagnetic resonance in medicine2021
Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI.
Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- 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
- Anatomical location-related hemodynamic variations are associated with atherosclerosis in the middle cerebral artery: a preliminary cross-sectional 4D flow and 3D vessel wall MRI study.Quantitative imaging in medicine and surgery · 2025Article
- Generalized Super-Resolution 4D Flow MRI - Using Ensemble Learning to Extend Across the Cardiovascular System.IEEE journal of biomedical and health informatics · 2024Article
- Evaluating the accuracy of cerebrovascular computational fluid dynamics modeling through time-resolved experimental validation.Scientific reports · 2024Article
- Automated intracranial vessel segmentation of 4D flow MRI data in patients with atherosclerotic stenosis using a convolutional neural network.Frontiers in radiology · 2024Article
- Observation of intracranial artery and venous sinus hemodynamics using compressed sensing-accelerated 4D flow MRI: performance at different acceleration factors.Frontiers in neuroscience · 2024Article
- Generalized super-resolution 4D Flow MRI-using ensemble learning to extend across the cardiovascular system.ArXiv · 2023Article
- Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning.The international journal of cardiovascular imaging · 2023Article
- Providing next-generation haemodynamic risk prediction through multiscale imaging of cardiovascular pressure gradients: the Horizon Europe MultiPRESS project.European heart journal · 2023Article
- Accelerated dual-venc 4D flow MRI with variable high-venc spatial resolution for neurovascular applications.Magnetic resonance in medicine · 2022Article
- Relative pressure estimation from 4D flow MRI using generalized Bernoulli equation in a phantom model of arterial stenosis.Magma (New York, N.Y.) · 2022Article
- Altered Aortic Hemodynamics and Relative Pressure in Patients with Dilated Cardiomyopathy.Journal of cardiovascular translational research · 2022Article
- Inverse problems in blood flow modeling: A review.International journal for numerical methods in biomedical engineering · 2022Review
- On the numerical treatment of viscous and convective effects in relative pressure reconstruction methods.International journal for numerical methods in biomedical engineering · 2022Article
- Influence of Spatial Resolution and Compressed SENSE Acceleration Factor on Flow Quantification with 4D Flow MRI at 3 Tesla.Tomography (Ann Arbor, Mich.) · 2022Article
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Authors and funding
10 authors.
Funding
Abstract
purposeHemodynamic alterations are indicative of cerebrovascular disease. However, the narrow and tortuous cerebrovasculature complicates image-based assessment, especially when quantifying relative pressure. Here, we present a systematic evaluation of image-based cerebrovascular relative pressure mapping, investigating the accuracy of the routinely used reduced Bernoulli (RB), the extended unsteady Bernoulli (UB), and the full-field virtual work-energy relative pressure (
methodsPatient-specific in silico models were used to generate synthetic cerebrovascular 4D Flow MRI, with RB, UB, and
resultsThe in silico analysis indicate that accurate relative pressure estimations are inherently coupled to spatial sampling: at dx = 1.0 mm high errors are reported for all methods; at dx = 0.5 mm
conclusionImage-based full-field methods such as
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.