Abstract
Pressure difference is an accepted clinical biomarker for cardiovascular disease conditions such as aortic coarctation. Currently, measurements of pressure differences in the clinic rely on invasive techniques (catheterization), prompting development of non-invasive estimates based on blood flow. In this work, we propose a non-invasive estimation procedure deriving pressure difference from the work-energy equation for a Newtonian fluid. Spatial and temporal convergence is demonstrated on in silico Phase Contrast Magnetic Resonance Image (PC-MRI) phantoms with steady and transient flow fields. The method is also tested on an image dataset generated in silico from a 3D patient-specific Computational Fluid Dynamics (CFD) simulation and finally evaluated on a cohort of 9 subjects. The performance is compared to existing approaches based on steady and unsteady Bernoulli formulations as well as the pressure Poisson equation. The new technique shows good accuracy, robustness to noise, and robustness to the image segmentation process, illustrating the potential of this approach for non-invasive pressure difference estimation.
Highlights
The measurement of pressure differences in current clinicalPressure differences, or pressure drops, measured over vascular segments are widely used clinically as biomarkers for a number of cardiovascular disorders (Baumgartner et al, 2009; Sawaya et al, 2012; Vahanian et al, 2007)
Other examples of pressure based metrics in the clinic include the transvalvular drop – an accepted metric to classify the severity of aortic valve stenosis (Baumgartner et al, 2009; De Bruyne et al, 2006; Feldman, 2006), the Left-Ventricle Outflow Tract (LVOT) pressure drop – used to define the guidelines for the treatment of Hypertrophic Cardiomyopathy (HCM) (Gersh et al, 2011), and the transstenotic pressure difference in the coronary artery – used to quantify the Fractional Flow Reserve (FFR) (Deng et al, 2014)
Four Dimensional Phase-Contrast Magnetic Resonance Imaging (MRI) (4D Phase Contrast Magnetic Resonance Image (PC-MRI)) data enables the solution of the Poisson Pressure Equation (PPE), where pressure is derived explicitly as a function of the acquired velocity field (Bock et al, 2011; Krittian et al, 2012), allowing the estimation of the convective effects in all spatial directions and the contribution of viscous dissipation (Lamata et al, 2014)
Summary
Pressure drops, measured over vascular segments are widely used clinically as biomarkers for a number of cardiovascular disorders (Baumgartner et al, 2009; Sawaya et al, 2012; Vahanian et al, 2007). Four Dimensional Phase-Contrast MRI (4D PC-MRI) data enables the solution of the Poisson Pressure Equation (PPE), where pressure is derived explicitly as a function of the acquired velocity field (Bock et al, 2011; Krittian et al, 2012), allowing the estimation of the convective effects in all spatial directions and the contribution of viscous dissipation (Lamata et al, 2014) This approach has been successfully applied for the estimation of the pressure in aortic coarctation (Riesenkampff et al, 2014). An alternative approach to estimate pressure differences in the vascular anatomy is based on 3D Computational Fluid Dynamics (CFD) simulations (Kim et al, 2010; LaDisa et al, 2011; Sankaran, 2012; Vignon-Clementel et al, 2010) In this case, patient specific geometric models are reconstructed from images such as computed tomography angiography and velocity boundary conditions are defined from flow measurements. We conclude by highlighting the benefits of the new approach and proposing possible improvements for translation of this technique into the clinic
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