  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
propagation. This combination of the arterial network parameters (representing
the VPs) and the haemodynamics waveforms of pressure and flow-rates at various
locations (representing functional response of the VPs) makes up the VPD. While
75,000 VPs are sampled from the posterior distribution, 10,000 are discarded as
the initial burn-in period. A further 12,857 VPs are subsequently removed due
to the presence of negative average flow-rate. Due to an undesirable behaviour
observed in some VPs -- asymmetric under- and over-damped pressure and
flow-rate profiles in the left and right sides of the arterial system -- a
filter is proposed for their removal. The final VPD has 28,868 subjects. It is
shown that the methodology is appropriate by comparing the VPD statistics to
those reported in literature across real populations. A good agreement between
the two is found while respecting physiological/geometrical constraints. The
pre-filter database is made available at
https://doi.org/10.5281/zenodo.4549764.

Medical Physics