In Vitro Major Arterial Cardiovascular Simulator to Generate Benchmark Data Sets for In Silico Model Validation

Cardiovascular diseases are commonly caused by atherosclerosis, stenosis and aneurysms. Understanding the influence of these pathological conditions on the circulatory mechanism is required to establish methods for early diagnosis. Different tools have been developed to simulate healthy and pathological conditions of blood flow. These simulations are often based on computational models that allow the generation of large data sets for further investigation. However, because 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 to develop and validate a hardware simulator to generate benchmark data sets of healthy and pathological conditions. The development process was led by specific design criteria to allow flexible and physiological simulations. The in vitro hardware simulator includes the major 33 arteries and is driven by a ventricular assist device generating a parametrised in-flow condition at the heart node. Physiologic flow conditions, including heart rate, systolic/diastolic pressure, peripheral resistance and compliance, are adjustable across a wide range. The pressure and flow waves at 17 + 1 locations are measured by inverted fluid-resistant pressure transducers and one ultrasound flow transducer, supporting a detailed analysis of the measurement data even for in silico modelling applications. The pressure and flow waves are compared to in vivo measurements and show physiological conditions. The influence of the degree and location of the stenoses on blood pressure and flow was also investigated. The results indicate decreasing translesional pressure and flow with an increasing degree of stenosis, as expected. The benchmark data set is made available to the research community for validating and comparing different types of computational models. It is hoped that the validation and improvement of computational simulation models will provide better clinical predictions.