Measuring arterial material properties using wave-based approaches with ultrasound and computational models

点击翻译按钮获取中文摘要

ABSTRACT/SUMMARY Background: Increased arterial stiffness has been associated with indicators of damage to the brain including presence of white matter hyperintensity, infarctions, and brain atrophy as assessed with magnetic resonance imaging (MRI). When vessels lose their ability to mechanically absorb the effects of pulsatile flow, that pulsatility is transmitted to the vasculature in the brain leading to deleterious changes in the brain tissue. Understanding the effect of ultrasound measured viscoelastic mechanical properties of central conduit (e.g., carotid) arteries relative to MRI measured brain health indicators could result in a very important tool for predicting and managing changes in brain function. This research project aims to address these unmet and critical needs to develop techniques for the accurate and translatable ultrasound measurement of viscoelastic arterial mechanical properties and evaluate associations with structural changes in the brain. Methods: Ultrasound is a first-line imaging modality for vascular evaluation, but most clinical scanners do not have the ability to evaluate the elastic or viscoelastic mechanical properties of vessels in an accurate, quantitative manner with high temporal resolution. In the first cycle of this grant, we developed an ultrasound- based method called arterial dispersion ultrasound vibrometry (ADUV) that utilizes acoustic radiation force to stimulate high-frequency (200-1500 Hz) waves in the arterial wall, followed by high frame rate ultrasound to measure the wave motion, which is used to characterize arterial viscoelastic mechanical properties. However, our research revealed that several aspects of ADUV could be further improved before being used on a daily basis in the clinic. Among these improvements are increasing the wave motion signal-to-noise ratio, making precise measurements of the vessel geometry with ultrasound, and improving our inversion and classification frameworks. With these improved ADUV methods, we will evaluate how the viscoelastic properties of the carotid artery are associated with the health of the brain as judged using MRI brain morphology data. With the strong collaborative team including expertise in cardiovascular medicine, radiology, ultrasound engineering, waveguide modeling, inverse problems, data reduction and classification, we will bring ADUV closer to widespread translation with the following Specific Aims: Aim 1) Optimize ADUV to enhance motion measurement quality for more accurate and precise mechanical property estimation. Aim 2) Develop advanced mathematical models for estimation of arterial mechanical properties and classification of disease state. Aim 3) Correlate ADUV measurements of carotid artery viscoelasticity with MRI indicators of brain health in patients.