In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark Data Sets for in-silico Model Validation

TL;DR

This paper presents an in-vitro hardware cardiovascular simulator that generates benchmark data for validating computational blood flow models, including healthy and pathological conditions like stenosis, with detailed pressure and flow measurements.

Contribution

Development and validation of a comprehensive in-vitro arterial simulator that produces benchmark data for model validation, capturing physiological and pathological blood flow conditions.

Findings

Pressure and flow waves match physiological conditions.

Stenosis degree correlates with decreased pressure and flow.

Benchmark data set is publicly available for model validation.

Abstract

A deeper understanding of the influence of common cardiovascular diseases like stenosis, aneurysm or atherosclerosis on the circulatory mechanism is required, to establish new methods for early diagnosis. Different types of simulators were developed in the past to simulate healthy and pathological conditions of blood flow, often based on computational models, which allow to generate large data sets. However, since computational models often lack some aspects of real world data, hardware simulators are used to close this gap and generate data for model validation. The aim of this study is the development and validation of a hardware simulator to generate benchmark data sets of healthy and pathological conditions. The in-vitro hardware simulator in this study includes the major 33 arteries and is driven by a ventricular assist device generating a parametrised input condition at the heart node. Physiologic flow conditions including heart rate, systolic/diastolic pressure, peripheral resistance and compliance are adjustable in a wide range. The pressure and flow waves at 17+1 different locations are measured by inverted fluid resistant pressure transducers and one ultrasound flow transducer supporting a detailed analysis of the measurement data. The pressure and flow waves show physiological conditions. Furthermore, the influence of stenoses degree and location on blood pressure and flow was investigated. The results indicate decreasing translesional pressure and flow with increasing degree of stenosis, as expected. The benchmark data set is made available to the research community, with the purpose to validate and compare in-silico models of different type.