Non-invasive Prediction of Peak Systolic Pressure Drop across Coarctation of Aorta using Computational Fluid Dynamics.
Non-invasive Prediction of Peak Systolic Pressure Drop across Coarctation of Aorta using Computational Fluid Dynamics.
复制标题
DOI：
10.1109/embc44109.2020.9176461
复制
发表时间：
2020-07
期刊：
影响因子：
--
通讯作者：
Krieger A
中科院分区：
文献类型：
--
作者：
Aslan S;Mass P;Loke YH;Warburton L;Liu X;Hibino N;Olivieri L;Krieger A
本文提出了一种新的方法，无创测量收缩压差（PSPD）的峰值横跨主动脉缩窄的诊断缩窄的严重程度。传统的无创超声压降估计可能会低估严重程度，而通过心导管插入术进行的有创测量可能会给患者带来风险。为了解决这个问题，我们采用计算流体动力学（CFD）计算，以准确地预测PSPD跨缩窄的心脏磁共振（CMR）成像数据和袖带压力测量的基础上，从一个手臂。一个病人特定的主动脉模型的边界条件指定在入口处的升主动脉通过使用随时间变化的血液速度，和降主动脉及主动脉上支的出口。为了估计Windkessel模型的参数，使用升主动脉、降主动脉和三个主动脉上分支中的两个中的时间平均流速进行稳态流模拟。在其中一个主动脉上分支的出口处指定一个臂的平均袖带压力。将CFD预测的5例患者（n=5）的PSPD与导管插入术获得的有创测量压降进行比较。使用完全无创的流量和袖带压力数据准确地预测了主动脉缩窄的PSPD（平均μ = 0.3mmHg，标准差σ = 4.3mmHg）。我们的研究结果表明，所提出的方法可能会取代侵入性测量估计缩窄的严重程度。收缩压峰值下降是主动脉缩窄严重程度的指标。可以使用CMR的无创袖带压力和流量数据进行预测，而不会给患者带来任何额外风险。
This paper proposes a novel method to non-invasively 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. 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.