Cardiovascular Simulations Can Revolutionize Point of Care

Feature Article

Cardiovascular Simulations Can Revolutionize Point of Care Diagnosis of Cardiovascular Disease! Introducing CardioFAN: A Novel Platform for Arterial Network Simulations

by Yashar Seyed Vahedein, RIT Engineering PhD Candidate

Cardiovascular diseases remain the number one factor for loss of life among humans in the 21 st century. According to American Heart Association, every 39 seconds 1 American dies from heart disease or stroke, and the direct and indirect costs for treatment are estimated to total more than $316 billion in US and $863 billion globally. However, there is still hope! Modern medicine and disease diagnostics are evolving drastically. Nowadays, disease diagnosis not only requires experience-based knowledge of the medical practitioner, it also utilizes the accurately engineered biomedical devices and test procedures to monitor health, predict abnormalities and treat the patients. This has led to a new era for accurately engineered biomedical devices and patient-specific diagnostics and testing.

Some of the common types of cardiovascular diseases are arterial aneurysm, arterial narrowing (stenosis), and hypertension. Historically invasive clinical tests were used to diagnose these problems. For instance, for aneurysms, if your doctor is concerned that you have one in your brain, you may get a CT scan or an invasive test called an angiogram. During this process, the doctors inject dye into an artery in an arm or leg, which then travels to your brain. An image of your brain is then taken. The dye will intensify the picture contrast in CT image and make it easier for your doctor to see potential problems. In case of a patient with stenosis, one of the arteries around the heart (i.e. coronary arteries) might have become narrowed. The diagnostic process is even more invasive here. The doctor inserts a long and narrow tube, called catheter, into your arteries or veins to reach the coronary arteries in order to measure local vital signals (blood pressure, cardiac output, etc.). The signals are used to assess if the person needs further treatment. Merits of replacing these clinical methods by more accessible and less invasive techniques or using them as a last resort is clear; the patients with cardiovascular problems are constantly suffering from the painful testing procedures and the high costs for each of these tests.

Clinical catheter insertion (left) and cardiovascular simulation based pressure measurements (center + right).

In Laboratory of Applied Nonlinear Mechanics (LANMech), we are trying to close the gap between the clinical tests and in-situ patient care by utilizing biomechanical and biofluidics simulations, allowing us to optimize the design of sophisticated devices and novel algorithms for diagnostics purposes. The aim is to provide novel algorithms for noninvasively monitoring patient’s cardiovascular health and analyze their physiological signals, such as blood pressure and cardiac output to detect possible abnormalities or changes. These algorithms need to be patientspecific, thereby we also need to have a process to easily calibrate them for each patient. This way each individual patient can continuously monitor their cardiovascular health, and in case of seeing abnormal behavior, the patient

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can contact their physician and send them the reported signals to get initial feedback. For the past 4 years, we have been working on this novel open source algorithm, titled CardioFAN, to noninvasively measure blood flow, pressure and cardiac output all over the patient’s cardiovascular system in normal, active and sick patients.

In the past, one of the main challenges that used to hinder advances in generating a digital twin for cardiovascular system was the inaccuracy of predictions, and long computational times, when compared to clinical measurements or experimental setups mimicking the actual geometry and physiology. With extensive testing and optimization of our physics-based algorithm, we made sure that given the mechanical properties of a patients vascular network and their heart function are known, it is possible for the simulation to correctly predict the blood pressure and flow signals during a complete heartbeat, i.e. cardiac cycle.

Comparison of the computational (red + blue) vs clinical (black) values for blood pressure, flow and vessel area

Beating of the human heart imposes a large surge of blood into his arteries. This results in the local expansion of arteries to compensate for the sudden increase in blood pressure. When the heart stops pushing blood into the system the arteries deflate in the second part of the heart cycle, and the cycle repeats itself. This generates a blood pressure pulse which travels all over the body with a certain speed. Some clinicians use the time that it takes for this pulse to arrive to specific body parts for diagnostic purposes. The advantage of CardioFAN is in its capability to capture complete mechanical behavior of blood flow and blood vessel wall, while keeping the computational times short. This allows us to calculate the speed and time of pulse propagation.

Each patient has a different combination of speed of pulse propagation, high/low blood pressure values and their heart can pump a specific amount of volume at each beat. We developed new techniques to calibrate our algorithm for each individual patient and therefore we can predict the combination of these values continuously. The new technology enables a path for measuring blood pressure and cardiac output without the need for catheters. Combined with the sensors in wearable devices, CardioFAN can be implemented in device software to enhance measurement capabilities from only heart rate to providing blood pressure, cardiac output and speed of pulse all over the body.

This new field in medicine is getting a lot of momentum for the level of accuracy that it provides while providing easy to access preliminary in-situ physiological data for both patients and medical doctors. It uses physics-based measurements that help the patients to continuously monitor their blood pressure and cardiac output, while allowing practitioners to decide the next steps in treatment or surgery with a stronger level of confidence. q

Yashar Seyed Vahedein is a doctoral candidate of the RIT engineering PhD program and a doctoral research assistant at Mechanical Engineering Department of RIT, developing computational models of circulatory system for disease diagnostics purposes. He received his MS degree in Mechanical Engineering with the thermal fluidics focus from Rochester Institute of Technology (RIT) in 2015, where he was conducting research on optimizing the carbon nanotube manufacturing process outcomes using Computational Fluid Dynamics (CFD) simulations. During his time as a MS student he also worked as a Graduate research and teaching assistant. Prior to that, He received his BS degree in Mechanical Engineering from the Azad University of Tehran – Central Branch in 2011, and he worked at Roshd Sanat Co. from 2011 until 2013 as an R&D mechanical design engineer.

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