ArticleScientific reports2021
Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI.Science advances · 2026Article
- AI-driven radiomics in vascular disease: a narrative review of image analysis and clinical translation.Cardiovascular diagnosis and therapy · 2026Review
- Performance analysis of a three-dimensional micromixer with baffles using a flexible physics-informed neural network.Scientific reports · 2026Article
- Physics constrained graph neural network for real time prediction of intracranial aneurysm hemodynamics.NPJ digital medicine · 2026Article
- Quantifying physiological variability and improving reproducibility in 4D-flow MRI cerebrovascular measurements with self-supervised deep learning.Magnetic resonance in medicine · 2025Article
- MR-AIV revealsbioRxiv : the preprint server for biology · 2025Article
- Predicting Cerebral Aneurysm Rupture: The Role of Computational Fluid Dynamics.Neuroimaging clinics of North America · 2025Review
- Imaging of Intracranial Aneurysms: A Review of Standard and Advanced Imaging Techniques.Translational stroke research · 2025Review
- 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
- Super-Resolving and Denoising 4D flow MRI of Neurofluids Using Physics-Guided Neural Networks.Annals of biomedical engineering · 2025Article
- Conditional neural field latent diffusion model for generating spatiotemporal turbulence.Nature communications · 2024Article
- Computational fluid dynamics of bladder voiding using 3D dynamic MRI.International journal for numerical methods in biomedical engineering · 2024Article
- Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning.Journal of biomechanical engineering · 2024Article
- Optimizing encoding strategies for 4D Flow MRI of mean and turbulent flow.Scientific reports · 2024Article
- Unraveling diurnal and technical variability in cerebral hemodynamics from neurovascular 4D-Flow MRI.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2024Article
- Four-dimensional flow MRI for quantitative assessment of cerebrospinal fluid dynamics: Status and opportunities.NMR in biomedicine · 2024Review
- Accelerated simulation methodologies for computational vascular flow modelling.Journal of the Royal Society, Interface · 2024Review
- Current Understanding of the Anatomy, Physiology, and Magnetic Resonance Imaging of Neurofluids: Update From the 2022 "ISMRM Imaging Neurofluids Study group" Workshop in Rome.Journal of magnetic resonance imaging : JMRI · 2024Review
- Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma.Cancers · 2024Article
- The Potential of Deep Learning to Advance Clinical Applications of Computational Biomechanics.Bioengineering (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Blood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to PC MRI. One method that excels in creating low-error, physics-based, velocity fields is computational fluid dynamics (CFD). Augmentation of cerebral 4D flow MRI data with CFD-informed neural networks may provide a method to produce highly accurate physiological flow fields. In this preliminary study, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a convolutional neural network, and then using the trained network to enhance MRI-derived velocity fields in cerebral blood vessel data sets. Through testing on simulated images, phantom data, and cerebrovascular 4D flow data from 20 patients, the trained network successfully de-noised flow images, decreased velocity error, and enhanced near-vessel-wall velocity quantification and visualization. Such image enhancement can improve experimental and clinical qualitative and quantitative cerebrovascular PC MRI analysis.
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.