Making heart health assessment accessible and effective with model-enhanced carotid ultrasound
Irene Suriani defended her PhD thesis at the Department of Electrical Engineering on February 18th.
Cardiovascular disease (CVD) is the world’s leading cause of death, with an ever-rising prevalence. The assessment of key markers of CVD risk can help with detecting the disease and starting preventative care in an early-stage. Yet, this remains a challenge due to the lack of practical, non-invasive tools for assessing arterial health. In her PhD research, Irene Suriani explores the use of model-enhanced carotid ultrasound (cUS) as a groundbreaking method for evaluating cardiovascular risk and monitoring critical care patients. The results of this research show that cUS can become a reliable, non-invasive alternative for cardiovascular monitoring.
Carotid ultrasound is a promising method for cardiovascular evaluation, because it provides measurements that tell a lot about the conditions of the arteries and blood flow through arteries of a patient. Next to that the common carotid artery is easily accessible. However, the challenge with this method is that it’s complex to interpretate carotid waveforms, as their shape is influenced by a variety of physiological factors. This makes it difficult to distract reliable clinical information. addresses this issue in her research by using computation modeling to simulate carotid waveforms and their relationship to cardiovascular properties.
Arterial stiffness as a key indicator
With the computer simulations Irene Suriani created a virtual population dataset to identify relationships between key physiological parameters and factors, such as age and gender. This research helps to highlight relevant markers of arterial stiffness, which is a key indicator of cardiovascular risk. The research also provides a novel, non-invasive method for estimating local carotid stiffness based on a model.
Patient-specific modeling
Irene Suriani also delves into the use of patient-specific modeling to enhance the accuracy of cUS for monitoring the waveforms of blood flow in the carotid artery. This results in a model-based approach to monitor cardiac-output changes in critical-care settings. This PhD research’s’ findings provide significant advancements for cardiovascular monitoring and early-stage CVD assessment. This has the potential to make heart health more accessible and effective.
Title of PhD thesis: . Promotor: Prof. Massimo Mischi and Prof. . Co-promotor: Dr. Jens Muehlsteff