Objective: Personalization of hemodynamic modeling plays a crucial role in functional prediction of the cardiovascular system (CVS). While reduced-order models of one-dimensional (1D) blood vessel models with zero-dimensional (0D) blood vessel and heart models have been widely recognized to be an effective tool for reasonably estimating the hemodynamic functions of the whole CVS, practical personalized models are still lacking. In this paper, we present a novel 0-1D coupled, personalized hemodynamic model of the CVS that can predict both pressure waveforms and flow velocities in arteries. Methods: We proposed a methodology by combining the multiscale CVS model with the Levenberg-Marquardt optimization algorithm for effectively solving an inverse problem based on measured blood pressure waveforms. Hemodynamic characteristics including brachial arterial pressure waveforms, artery diameters, stroke volumes, and flow velocities were measured noninvasively for 62 volunteers aged from 20 to 70 years for developing and validating the model. Results: The estimated arterial stiffness shows a physiologically realistic distribution. The model-fitted individual pressure waves have an averaged mean square error (MSE) of 7.1 mmHg $^2$ ; simulated blood flow velocity waveforms in carotid artery match ultrasound measurements well, achieving an average correlation coefficient of 0.911. Conclusion: The model is efficient, versatile, and capable of obtaining well-fitting individualized pressure waveforms while reasonably predicting flow waveforms. Significance: The proposed methodology of personalized hemodynamic modeling may therefore facilitate individualized patient-specific assessment of both physiological and pathological functions of the CVS.

Atherosclerotic intracranial internal carotid artery stenosis (IICAS) is a leading cause of strokes. Due to the limitations of major cerebral imaging techniques, the early diagnosis of IICAS remains challenging. Clinical studies have revealed that arterial stenosis may have complicated effects on the blood flow’s velocity from a distance. Therefore, based on a patient-specific one-dimensional hemodynamic model, we quantitatively investigated the effects of IICAS on extracranial internal carotid artery (ICA) flow velocity waveforms to identify sensitive hemodynamic indices for IICAS diagnoses. Classical hemodynamic indices, including the peak systolic velocity (PSV), end-diastolic velocity (EDV), and resistive index (RI), were calculated on the basis of simulations with and without IICAS. In addition, the first harmonic ratio (FHR), which is defined as the ratio between the first harmonic amplitude and the sum of the amplitudes of the 1st–20th order harmonics, was proposed to evaluate flow waveform patterns. To investigate the diagnostic performance of the indices, we included 52 patients with mild-to-moderate IICAS ( 0.05) between the two groups, except for the PSV of the right ICA (p = 0.011). The FHR was significantly higher in the stenosis group than in the control group (p < 0.001), with superior diagnostic performance. Taken together, the FHR is a promising index for the early diagnosis of IICAS using carotid Doppler ultrasound methods.

Arterial stiffness is a pivotal predictor of cardiovascular events, yet its in vivo characterization remains elusive because arterial wall mechanics cannot be directly measured. Conventional inverse approaches require high-resolution imaging to resolve wall motion, restricting their clinical applicability. Here, we present a non-invasive computational framework that infers regional arterial stiffness solely from routinely acquired pressure and blood flow velocity waveforms by leveraging personalized one-dimensional haemodynamic models of the human arterial network. Patient-specific models were constructed for 21 individuals with hypertension using stroke volume, arterial diameters, brachial pressures and carotid flow profiles. Regional stiffness parameters were identified through optimization to reconcile simulated and measured haemodynamics, and validated against brachial-ankle pulse wave velocity. The inferred stiffness demonstrated strong concordance with clinical measurements (r = 0.81; standard error = 279 cm s)–1. Longitudinal evaluation in 13 patients further revealed therapy-dependent remodelling, with calcium channel blockers selectively reducing pathological stiffening while preserving compliant vessels. Together, these results establish model-based inversion of standard haemodynamic data as a practical, scalable approach for quantifying arterial stiffness, enabling individualized cardiovascular risk assessment and treatment monitoring.