Article

Machine Learning for Aiding Blood Flow Velocity Estimation Based on Angiography

2022-09-29

Abstract excerpt

Detailed flow distributions in vascular systems are the key to identifying hemodynamic risk factors for the development and progression of vascular diseases. Although computational fluid dynamics (CFD) has been widely used in bioengineering research on hemodynamics predictions, not only are high-fidelity CFD simulations time-consuming and computing-expensive, but also not friendly to clinical applications due to t...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: fff0564c-01d4-58b1-88d3-8aad17ae3027
DOI: 10.20944/preprints202209.0460.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Machine Learning for Aiding Blood Flow Velocity Estimation Based on AngiographyDOI 10.20944/preprints202209.0460.v1Read

Generating retinal flow maps from structural optical coherence tomography with artificial intelligenceSemantic proximity Read

Segmentation and Measurements of Major Blood Vessels from CT Pulmonary Angiography Using Deep LearningSemantic proximity Read

AUTOMATION AND RELIABILITY IN ENDOVASCULAR IMAGING: A FRAMEWORK FOR AI-AUGMENTED QUANTITATIVE ANGIOGRAPHYSemantic proximity Read

Deep graph convolutional neural network for one-dimensional hepatic vascular haemodynamic predictionSemantic proximity Read

Perfusion estimation using MRI-based measurements and a porous media flow modelSemantic proximity Read

Comparing Machine Learning Approaches for Predicting CFD-Derived Stroke Risk Indicators in Atrial Fibrillation PatientsSemantic proximity Read

Physics-Informed AI Framework Enables Generalizable and Reliable Cerebrovascular Hemodynamic ProfilingSemantic proximity Read

Validation of Machine-Learning Angiography-Derived Physiological Pattern of Coronary Artery DiseaseSemantic proximity Read

Cerebrovascular super-resolution 4D Flow MRI – using deep learning to non-invasively quantify velocity, flow, and relative pressureSemantic proximity Read

A deep learning approach for improving two-photon vascular imaging speedsSemantic proximity Read

Anatomical modeling of brain vasculature in two-photon microscopy by generalizable deep learningSemantic proximity Read

Next-Generation Non-Invasive Capillary Angiography SystemSemantic proximity Read