ArticleUltrasound in medicine & biology2025
Estimation of In Vivo Human Carotid Artery Elasticity Using Arterial Dispersion Ultrasound Vibrometry.
Article in Ultrasound in medicine & biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Noninvasive Assessment of Arterial Compliance and Lumen Pressure in Human Carotid Arteries Using Physics-Informed Neural Networks and Ultrasound Imaging: A Clinical Feasibility Study.Research square · 2026Article
- Assessment of Spatially-Varying Arterial Wall Stiffness and Pressure Using a Physics-Informed Neural Network and Pulse Wave Imaging: An in Silico and Experimental Phantom Study of Stenotic Vessels.Ultrasound in medicine & biology · 2026Article
- The Geometric Dependence of Wave Velocity in Carotid Arteries: Phantom and Finite Element Study and Implications for Vascular "Shear Wave" Elastography.Ultrasound in medicine & biology · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveArterial 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.
methodsWe 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. RESULTS AND
conclusionsWith 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.
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