A physiologically realistic virtual patient database for the study of arterial haemodynamics
Gareth Jones, Jim Parr, Perumal Nithiarasu, Sanjay Pant

TL;DR
This paper develops a physiologically realistic virtual patient database for arterial haemodynamics, using Bayesian methods and MCMC sampling, to facilitate the study of arterial diseases and their effects on blood flow and pressure.
Contribution
It introduces a novel framework for creating a large, physiologically constrained virtual patient database by integrating available data and constraints through Bayesian modeling and MCMC sampling.
Findings
The final database contains 28,868 virtual patients.
The methodology shows good agreement with real population data.
A filtering process ensures physiologically plausible profiles.
Abstract
This study creates a physiologically realistic virtual patient database (VPD), representing the human arterial system, for the primary purpose of studying the affects of arterial disease on haemodynamics. A low dimensional representation of an anatomically detailed arterial network is outlined, and a physiologically realistic posterior distribution for its parameters is constructed through a Bayesian approach. This approach combines both physiological/geometrical constraints and the available measurements reported in the literature. A key contribution of this work is to present a framework for including all such available information for the creation of virtual patients (VPs). The Markov Chain Monte Carlo (MCMC) method is used to sample random VPs from this posterior distribution, and the pressure and flow-rate profiles associated with the VPs are computed through a model of pulse wave…
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