Non-invasive Prediction of Peak Systolic Pressure Drop across Coarctation of Aorta using Computational Fluid Dynamics

Abstract

This paper proposes a novel method to noninvasively measure the peak systolic pressure difference (PSPD) across coarctation of the aorta for diagnosing the severity of coarctation. Traditional non-invasive estimates of pressure drop from the ultrasound can underestimate the severity and invasive measurements by cardiac catheterization can carry risks for patients. To address the issues, we employ computational fluid dynamics (CFD) computation to accurately predict the PSPD across a coarctation based on cardiac magnetic resonance (CMR) imaging data and cuff pressure measurements from one arm. The boundary conditions of a patient-specific aorta model are specified at the inlet of the ascending aorta by using the time-dependent blood velocity, and the outlets of descending aorta and supra aortic branches by using a 3-element Windkessel model. To estimate the parameters of the Windkessel model, steady flow simulations were performed using the time-averaged flow rates in the ascending aorta, descending aorta, and two of the three supra aortic branches. The mean cuff pressure from one arm was specified at the outlet of one of the supra aortic branches. The CFD predicted PSPDs of 5 patients (n=5) were compared with the invasively measured pressure drops obtained by catheterization. The PSPDs were accurately predicted (mean µ=0.3mmHg, standard deviation σ =4.3mmHg) in coarctation of the aorta using completely non-invasive flow and cuff pressure data. The results of our study indicate that the proposed method could potentially replace invasive measurements for estimating the severity of coarctations.Clinical relevance-Peak systolic pressure drop is an indicator of the severity of coarctation of the aorta. It can be predicted without any additional risks to patients using non-invasive cuff pressure and flow data from CMR.

Fig. 1.

(a) 3-D geometry of aorta after segmentation and smoothing process, (b) time-dependent flow rates in the ascending and descending aorta obtained by CMR, (c) picture of a typical cuff device that is used in infants to measure mean pressure from one arm (right arm in this figure) non-invasively, (d) The model with inlet and outlet (O1, O2, O3, and O4) surfaces where the following boundary conditions are specified: flow rates at the inlet, O1, O2, and O3; and mean cuff pressure at O4 in this example, for steady simulations, (e) schematic of Windkessel model that is used at the outlet boundaries and resistances that are calculated using the mean pressures obtained by steady simulations, and (f) boundary conditions for the transient simulations

Fig. 2.

Pressure distributions in five aortas with coarctation. Since the peak pressure occurs at different times in AAo and DAo, the pressure distributions of the areas proximal and distal to the coarctation were individually extracted at their respective peak times and juxtaposed for visualization.