ArticleFrontiers in cardiovascular medicine2021
WSSNet: Aortic Wall Shear Stress Estimation Using Deep Learning on 4D Flow MRI.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Fourier Shell Analysis: k-Space-Based Metrics for Assessing Super-Resolution in 4D Flow MRI.Magnetic resonance in medicine · 2026Article
- Clinical and Computational Analysis of Left Subclavian Artery Coverage on High-Risk Blunt Thoracic Aortic Injury.Journal of clinical medicine · 2026Article
- Clinical applications of 4D flow magnetic resonance imaging in hepatic vascular disorders: practical biomarkers, protocol design, and translational implementation.Quantitative imaging in medicine and surgery · 2026Review
- Cardiac mechanotransduction from development to disease.APL bioengineering · 2026Review
- Impact of Partial Echo on 4D Flow MRI: The Insight From Synthetic MRI.Magnetic resonance in medicine · 2026Article
- Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling.Advanced healthcare materials · 2026Review
- Physics constrained graph neural network for real time prediction of intracranial aneurysm hemodynamics.NPJ digital medicine · 2026Article
- History and emerging trends of computational fluid dynamics applications in atherosclerosis: a bibliometric analysis and narrative review.Annals of medicine and surgery (2012) · 2026Review
- Article
- Anesthetic Management for Proximal Aortic Repair.Seminars in cardiothoracic and vascular anesthesia · 2025Review
- Super-resolution left ventricular flow and pressure mapping by Navier-Stokes-informed neural networks.Computers in biology and medicine · 2025Article
- Integrated multidisciplinary approach to aneurysm hemodynamic analysis: numerical simulation,Frontiers in bioengineering and biotechnology · 2025Review
- Algorithmic Generation of Parameterized Geometric Models of the Aortic Valve and Left Ventricle.Sensors (Basel, Switzerland) · 2024Article
- Developing a nearly automated open-source pipeline for conducting computational fluid dynamics simulations in anterior brain vasculature: a feasibility study.Scientific reports · 2024Article
- Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning.Journal of biomechanical engineering · 2024Article
- Super-resolution Left Ventricular Flow and Pressure Mapping by Navier-Stokes-Informed Neural Networks.bioRxiv : the preprint server for biology · 2024Article
- Rapid prediction of wall shear stress in stenosed coronary arteries based on deep learning.Frontiers in bioengineering and biotechnology · 2024Article
- Deep learning based assessment of hemodynamics in the coarctation of the aorta: comparison of bidirectional recurrent and convolutional neural networks.Frontiers in physiology · 2024Article
- Machine Learning and the Conundrum of Stroke Risk Prediction.Arrhythmia & electrophysiology review · 2023Review
- Advances in machine learning applications for cardiovascular 4D flow MRI.Frontiers in cardiovascular medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wall shear stress (WSS) is an important contributor to vessel wall remodeling and atherosclerosis. However, image-based WSS estimation from 4D Flow MRI underestimates true WSS values, and the accuracy is dependent on spatial resolution, which is limited in 4D Flow MRI. To address this, we present a deep learning algorithm (WSSNet) to estimate WSS trained on aortic computational fluid dynamics (CFD) simulations. The 3D CFD velocity and coordinate point clouds were resampled into a 2D template of 48 × 93 points at two inward distances (randomly varied from 0.3 to 2.0 mm) from the vessel surface ("velocity sheets"). The algorithm was trained on 37 patient-specific geometries and velocity sheets. Results from 6 validation and test cases showed high accuracy against CFD WSS (mean absolute error 0.55 ± 0.60 Pa, relative error 4.34 ± 4.14%, 0.92 ± 0.05 Pearson correlation) and noisy synthetic 4D Flow MRI at 2.4 mm resolution (mean absolute error 0.99 ± 0.91 Pa, relative error 7.13 ± 6.27%, and 0.79 ± 0.10 Pearson correlation). Furthermore, the method was applied on
Indexed as
Identifiers
What OpenQuestion holds
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.