Resting myocardial blood flow quantification using contrast-enhanced magnetic resonance imaging in the presence of stenosis: A computational fluid dynamics study

2015 ◽  
Vol 42 (7) ◽  
pp. 4375-4384 ◽  
Author(s):  
Karsten Sommer ◽  
Dominik Bernat ◽  
Regine Schmidt ◽  
Hanns-Christian Breit ◽  
Laura M. Schreiber
Keyword(s):  
Magnetic Resonance Imaging ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Blood Flow ◽  
Magnetic Resonance ◽  
Myocardial Blood Flow ◽  
Flow Quantification ◽  
Resonance Imaging ◽  
Blood Flow Quantification ◽  
Contrast Enhanced

Myocardial blood flow estimates from dynamic contrast-enhanced magnetic resonance imaging: three quantitative methods

2018 ◽  
Vol 63 (3) ◽  
pp. 035008 ◽  
Author(s):  
Cristian Borrazzo ◽  
Nicola Galea ◽  
Massimiliano Pacilio ◽  
Luisa Altabella ◽  
Enrico Preziosi ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Blood Flow ◽  
Magnetic Resonance ◽  
Myocardial Blood Flow ◽  
Quantitative Methods ◽  
Resonance Imaging ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

Four-dimensional velocity-encoded magnetic resonance imaging improves blood flow quantification in patients with complex accelerated flow

2012 ◽  
Vol 37 (1) ◽  
pp. 208-216 ◽  
Author(s):  
Sarah Nordmeyer ◽  
Eugénie Riesenkampff ◽  
Daniel Messroghli ◽  
Siegfried Kropf ◽  
Johannes Nordmeyer ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Blood Flow ◽  
Magnetic Resonance ◽  
Flow Quantification ◽  
Resonance Imaging ◽  
Blood Flow Quantification ◽  
Accelerated Flow

scholarly journals Assessing the methodology used to study the ascending aorta haemodynamics in bicuspid aortic valve

2021 ◽  
Author(s):  
Joy Edlin ◽  
Justin Nowell ◽  
Chris Arthurs ◽  
Alberto Figueroa ◽  
Marjan Jahangiri
Keyword(s):  
Magnetic Resonance Imaging ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Blood Flow ◽  
Magnetic Resonance ◽  
Aortic Valve ◽  
Computer Modelling ◽  
Ascending Aorta ◽  
Resonance Imaging ◽  
Computational Fluid Dynamics Analysis

Abstract Background Modern imaging techniques provide evermore-detailed anatomical and physiological information for use in computational fluid dynamics to predict the behaviour of physiological phenomena. Computer modelling can help plan suitable interventions. Our group used magnetic resonance imaging and computational fluid dynamics to study the haemodynamic variables in the ascending aorta in patients with bicuspid aortic valve before and after isolated tissue aortic valve replacement. Computer modelling requires turning a physiological model into a mathematical one, solvable by equations that undergo multiple iterations in four dimensions. Creating these models involves several steps with manual inputs, making the process prone to errors and limiting its inter- and intra-operator reproducibility. Despite these challenges we created computational models for each patient to study ascending aorta blood flow before and after surgery. Method Magnetic resonance imaging provided the anatomical and velocity data required for the blood flow simulation. Patient-specific in- and outflow boundary conditions were used for the computational fluid dynamics analysis. Results Haemodynamic variables pertaining to blood flow pattern and derived from the magnetic resonance imaging data were calculated. However, we encountered problems in our multi-step methodology, most notably processing the flow data. This meant that other variables requiring computation with computational fluid dynamics could not be calculated. Conclusion Creating a model for computational fluid dynamics analysis is as complex as the physiology under scrutiny. We discuss some of the difficulties associated with creating such models, along with suggestions for improvements in order to yield reliable and beneficial results.

scholarly journals Enhancing Magnetic Resonance Imaging With Computational Fluid Dynamics

2019 ◽  
Vol 2 (4) ◽  
Author(s):  
Giacomo Annio ◽  
Ryo Torii ◽  
Ben Ariff ◽  
Declan P. O'Regan ◽  
Vivek Muthurangu ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Blood Flow ◽  
Magnetic Resonance ◽  
Patient Specific ◽  
Human Aorta ◽  
Resonance Imaging ◽  
4D Flow ◽  
4D Flow Cmr

Abstract The analysis of the blood flow in the great thoracic arteries does provide valuable information about the cardiac function and can diagnose the potential development of vascular diseases. Flow-sensitive four-dimensional flow cardiovascular magnetic resonance imaging (4D flow CMR) is often used to characterize patients' blood flow in the clinical environment. Nevertheless, limited spatial and temporal resolution hinders a detailed assessment of the hemodynamics. Computational fluid dynamics (CFD) could expand this information and, integrated with experimental velocity field, enable to derive the pressure maps. However, the limited resolution of the 4D flow CMR and the simplifications of CFD modeling compromise the accuracy of the computed flow parameters. In this article, a novel approach is proposed, where 4D flow CMR and CFD velocity fields are integrated synergistically to obtain an enhanced MR imaging (EMRI). The approach was first tested on a two-dimensional (2D) portion of a pipe, to understand the behavior of the parameters of the model in this novel framework, and afterwards in vivo, to apply it to the analysis of blood flow in a patient-specific human aorta. The outcomes of EMRI are assessed by comparing the computed velocities with the experimental one. The results demonstrate that EMRI preserves flow structures while correcting for experimental noise. Therefore, it can provide better insights into the hemodynamics of cardiovascular problems, overcoming the limitations of MRI and CFD, even when considering a small region of interest. EMRI confirmed its potential to provide more accurate noninvasive estimation of major cardiovascular risk predictors (e.g., flow patterns, endothelial shear stress) and become a novel diagnostic tool.

Sign in / Sign up

Export Citation Format

Share Document