University of Arkansas, Department of Mechanical Engineering, Fayetteville, AR, USA.
University of Arkansas, Department of Biomedical Engineering, Fayetteville, AR, USA; University of Arkansas for Medical Sciences, Department of Surgery, Little Rock, AR, USA.
J Biomech. 2024 May;168:112124. doi: 10.1016/j.jbiomech.2024.112124. Epub 2024 Apr 29.
Congenital arterial stenosis such as supravalvar aortic stenosis (SVAS) are highly prevalent in Williams syndrome (WS) and other arteriopathies pose a substantial health risk. Conventional tools for severity assessment, including clinical findings and pressure gradient estimations, often fall short due to their susceptibility to transient physiological changes and disease stage influences. Moreover, in the pediatric population, the severity of these and other congenital heart defects (CHDs) often restricts the applicability of invasive techniques for obtaining crucial physiological data. Conversely, evaluating CHDs and their progression requires a comprehensive understanding of intracardiac blood flow. Current imaging modalities, such as blood speckle imaging (BSI) and four-dimensional magnetic resonance imaging (4D MRI) face limitations in resolving flow data, especially in cases of elevated flow velocities. To address these challenges, we devised a computational framework employing zero-dimensional (0D) lumped parameter models coupled with patient-specific reconstructed geometries pre- and post-surgical intervention to execute computational fluid dynamic (CFD) simulations. This framework facilitates the analysis and visualization of intricate blood flow patterns, offering insights into geometry and flow dynamics alterations impacting cardiac function. In this study, we aim to assess the efficacy of surgical intervention in correcting an extreme aortic defect in a patient with WS, leading to reductions in wall shear stress (WSS), maximum velocity magnitude, pressure drop, and ultimately a decrease in cardiac workload.
先天性动脉狭窄,如主动脉瓣上狭窄(SVAS),在威廉姆斯综合征(WS)和其他血管病变中非常普遍,这些病变对健康构成了重大风险。用于严重程度评估的传统工具,包括临床发现和压力梯度估计,由于其易受短暂生理变化和疾病阶段影响,往往不够准确。此外,在儿科人群中,这些和其他先天性心脏病(CHD)的严重程度通常限制了获得关键生理数据的侵入性技术的适用性。相反,评估 CHD 及其进展需要全面了解心内血流。当前的成像方式,如血流散斑成像(BSI)和四维磁共振成像(4D MRI),在解析流量数据方面存在局限性,尤其是在流速较高的情况下。为了解决这些挑战,我们设计了一个计算框架,采用零维(0D)集中参数模型,并结合术前和术后患者特定的重建几何形状,执行计算流体动力学(CFD)模拟。该框架便于分析和可视化复杂的血流模式,深入了解影响心脏功能的几何形状和流动力学变化。在这项研究中,我们旨在评估手术干预纠正 WS 患者严重主动脉缺陷的效果,从而降低壁面切应力(WSS)、最大速度幅度、压降,最终降低心脏工作量。