An approach for detecting hemodynamic parameters in the carotid arterial system based on the priori relationship between blood pressure and arterial radius

Cardio-cerebrovascular diseases are the leading causes of death worldwide, primarily driven by atherosclerosis. The common carotid artery (CCA) offers valuable insight into disease progression, as local hemodynamic parameters such as wall shear stress (WSS) influence plaque formation, while systemic indices like peripheral resistance reflect overall vascular health. Existing imaging-based approaches can quantify these measures but are costly, complex, and unsuitable for continuous monitoring. To address this, we developed a framework that integrates clinically acquired CCA waveforms with a priori modeling. By establishing a priori pressure–arterial radius (p– \(r_i\) ) relationship, dynamic \(r_i\) changes were estimated directly from continuous p signals. This relationship, combined with p and flow velocity waveforms, enabled a distributed–lumped parameter hybrid model to compute both local and systemic hemodynamic indices. The p– \(r_i\) model reliably reproduced \(r_i\) waveforms and exhibited temporal stability across repeated measurements over five weeks. Coupled with the hybrid model, it provided real-time estimates of local parameters such as flow rate and WSS, as well as systemic indices including resistance and compliance. Sensitivity analysis underscored the impact of variations in elastic parameters on outcomes. This approach supports continuous, non-invasive vascular monitoring with promising potential for future applications in early cardiovascular risk assessment.