Personalized Hemodynamic Modeling of the Human Cardiovascular System: A Reduced-Order Computing Model

Xiangdong Zhang,Hao Liu,Ye Li,Dan Wu,Fen Miao

doi:10.1109/tbme.2020.2970244

Abstract

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. 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. The estimated arterial stiffness shows a physiologically realistic distribution. The model-fitted individual pressure waves have an averaged mean square error (MSE) of 7.1mmHg2; simulated blood flow velocity waveforms in carotid artery match ultrasound measurements well, achieving an average correlation coefficient of 0.911. The model is efficient, versatile, and capable of obtaining well-fitting individualized pressure waveforms while reasonably predicting flow waveforms. The proposed methodology of personalized hemodynamic modeling may therefore facilitate individualized patient-specific assessment of both physiological and pathological functions of the CVS.

Highlights

P ERSONALIZED or patient-specific cardiovascular modeling has developed rapidly with varieties of clinical applications in recent years
We proposed a methodology by combining the 0-1D coupled hemodynamic model with the Levenberg–Marquardt optimization algorithm for effectively solving an inverse problem and developed a novel personalized cardiovascular system (CVS) model that can predict both pressure waveforms and flow velocities in arteries
Our results indicate that the estimated arterial stiffness has a physiologically realistic distribution; our personalized models can fit various blood pressure waveforms and simulated blood flow velocity waveforms match ultrasound measurements well

Summary

Introduction

P ERSONALIZED or patient-specific cardiovascular modeling has developed rapidly with varieties of clinical applications in recent years. Based on coronary CT angiography imaging, Taylor et al [3] established patient-specific CFD models of coronary arteries, which enabled the noninvasive estimation of fractional flow reserve, an index reflecting coronary stenosis severity. Spilker et al [4] coupled a 3D proximal pulmonary artery model with morphometry-based impedance boundary conditions, which was applied to a 16-year-old patient with repaired tetralogy of Fallot and which predicted nonsignificant hemodynamic improvement with removal of the stenosis. Hsia et al [5] used 3D computational models of the aortic arch to simulate outcomes of different surgery approaches. They suggested that the hybrid palliation approach may provide inferior systemic and cerebral blood flow rates in patients with hypoplastic left heart syndrome

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