Roy Tuhin, Lee Hyoung-Ki, Capron Charles B, Lopez-Jimenez Francisco, Hesley Gina K, Greenleaf James F, Urban Matthew W, Guddati Murthy N
Department of Civil Engineering, NC State University, Raleigh, NC, USA.
Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Ultrasound Med Biol. 2025 Feb;51(2):250-261. doi: 10.1016/j.ultrasmedbio.2024.09.023. Epub 2024 Oct 28.
Arterial stiffening serves as an early indicator for a variety of cardiovascular diseases. Arterial Dispersion Ultrasound Vibrometry (ADUV) is a method that leverages acoustic radiation force to stimulate arterial wall motion, assess wave propagation characteristics, and subsequently calculate the arterial shear modulus. Previously, we introduced an inversion technique based on a guided cylindrical wave model, which proved effective in rubber tube phantom experiments. In this study, we broaden the scope of our investigation from phantom experiments to in vivo examination of common carotid arteries in human subjects, identify the challenges, and provide solutions, leading to a systematic protocol for ADUV application and robust estimation of the elastic modulus of common carotid arteries.
We achieve this by analyzing ADUV data from 59 subjects categorized as (a) confirmed atherosclerotic cardiovascular disease (n = 27), (b) with cardiovascular risk factors (n = 20), and (c) healthy (n = 12). A crucial aspect of this work is the development of metrics to differentiate high-quality ADUV data from unusable data.
With the proposed metrics, in our cohort, we observed 82% of diameter data and 78% of motion data as usable data. Future work will involve applying this protocol to a larger cohort with subsequent statistical analysis to assess and validate the resulting biomarkers.
动脉僵硬度是多种心血管疾病的早期指标。动脉弥散超声振动测量法(ADUV)是一种利用声辐射力刺激动脉壁运动、评估波传播特性并随后计算动脉剪切模量的方法。此前,我们引入了一种基于圆柱导波模型的反演技术,该技术在橡胶管模型实验中证明是有效的。在本研究中,我们将研究范围从模型实验扩展到对人类受试者颈总动脉的体内检查,识别挑战并提供解决方案,从而形成一个用于ADUV应用和颈总动脉弹性模量稳健估计的系统方案。
我们通过分析59名受试者的ADUV数据来实现这一目标,这些受试者分为以下三类:(a)确诊的动脉粥样硬化性心血管疾病患者(n = 27),(b)有心血管危险因素的患者(n = 20),以及(c)健康受试者(n = 12)。这项工作的一个关键方面是开发用于区分高质量ADUV数据和不可用数据的指标。
使用所提出的指标,在我们的队列中,我们观察到82%的直径数据和78%的运动数据为可用数据。未来的工作将包括将该方案应用于更大的队列,并随后进行统计分析,以评估和验证所得的生物标志物。