In Vivo Quantification of Arterial Active Mechanics Using Deep Learning-Assisted Pressure-Area Analysis
Yuxuan Jiang, Yanping Cao

TL;DR
This study introduces a deep learning-based pressure-area analysis framework to quantify active arterial mechanics in vivo, distinguishing smooth muscle contributions during physiological stress with high accuracy.
Contribution
We developed an integrated ultrasound and neural network approach to measure active arterial mechanics, enabling differentiation of smooth muscle activity from passive stiffness in vivo.
Findings
Active mechanics remained elevated for ~15 minutes post-exercise.
Blood pressure normalized within ~5 minutes, showing dissociation from active mechanics.
Neural network segmentation demonstrated high spatial and temporal accuracy.
Abstract
Active arterial mechanics, governed by vascular smooth muscle contraction, are critical to physiological regulation, cardiovascular disease progression, and clinical diagnosis. Although various in vivo methods have been developed to assess arterial stiffness, most cannot distinguish the contribution of smooth muscle tone; therefore, quantitative characterization of arterial activity remains challenging. In this study, we developed a pressure-area analysis framework integrating ultrasound imaging, blood pressure measurement, neural network-based segmentation of arterial cross-sectional area, and biomechanical model-driven inversion to infer active mechanical properties. A total of 233 volunteers (aged 18-65 year) were recruited to acquire cross-sectional ultrasound videos of the right common carotid artery for training the neural network. The segmentation results demonstrate good spatial…
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Taxonomy
TopicsCardiovascular Health and Disease Prevention · Elasticity and Material Modeling · Cardiovascular Function and Risk Factors
