Automatic Tool for Pulmonary Artery Hemodynamic Assessment from 4D flow MRI
Autores: Haizea Erostarbe Garcia Maialen Stephens Txurio Ángel Gaitán Rahul Kumar Jorge Nuche Irene Marco Juan Delgado Jesús Ruis
Fecha: 01.04.2023
Abstract
Currently, the extraction of blood flow biomarkers to characterize diseases is a time consuming process as it requires the manual segmentation of vascular structures and complex computational fluid dynamics (CFD) simulations. Here, we propose a tool to automatically segment the pulmonary artery and to compute biomarkers from 4D flow magnetic resonance images. In the context of Pulmonary Hypertension (PH), we show that biomarkers such as peak velocity, flow rate, helicity and vorticity provide discriminative power between patient groups and are derived in a faster and simpler way than traditional methods.
BIB_text
title = {Automatic Tool for Pulmonary Artery Hemodynamic Assessment from 4D flow MRI},
keywds = {
4D flow MRI; automatic blood flow biomarkers; computational fluid dynamics; deep learning
}
abstract = {
Currently, the extraction of blood flow biomarkers to characterize diseases is a time consuming process as it requires the manual segmentation of vascular structures and complex computational fluid dynamics (CFD) simulations. Here, we propose a tool to automatically segment the pulmonary artery and to compute biomarkers from 4D flow magnetic resonance images. In the context of Pulmonary Hypertension (PH), we show that biomarkers such as peak velocity, flow rate, helicity and vorticity provide discriminative power between patient groups and are derived in a faster and simpler way than traditional methods.
}
isbn = {978-166547358-3},
date = {2023-04-01},
}
