Leonardo Geronzi

Early Stage Researcher 02

I’m Leonardo Geronzi and I come from Viterbo, a city of about 65,000 people in the north of Rome. With a strong interest in medicine and a passion for engineering, I chose to attend biomedical engineering at the University of Pisa, where I graduated in December 2019. I have always been fascinated by the cardiovascular field and the simulation studies in this domain have literally impressed me. This is why I performed numerical simulation of the aortic valve in my thesis.

I immediately saw a great opportunity in the MeDiTATe project: being able to work in such a large network of professors, surgeons, technicians and engineers is truly inspiring. It’s a challenge that motivates me to work with enthusiasm every day. 

As Early Stage Researcher 02, my PhD project in MeDiTATe is divided into 18 months at ANSYS Lyon and 18 months at the University of Rome “Tor Vergata”. My research concerns the study of the evolution and rupture of the Ascending Aortic Aneurysm. In the treatment of this pathology, the assessment of the diameter is the only criterion to decide if the Ascending Aorta surgery should be performed. In collaboration with the French Society of Thoracic and Cardiovascular Surgery, we want to propose a procedure to move from the images of the vessel directly to the analysis of the simulation results.

The goal and greatest ambition of this project is the revolution of the clinical practice. For this purpose, we are developing a prototype, which the clinician himself can use, to quickly obtain geometric measurements on the aortic model to monitor the progression of the pathology. We are then exploiting the full power of Reduced Order Models (ROMs) to obtain real-time results, providing crucial information on the haemodynamics. In addition to diameter evaluation, the risk of rupture can in fact be estimated by combining metrics and fluid-dynamic biomarkers. The ROM itself will be the basis for obtaining the so-called Medical Digital Twin (MDT): the clinician will be able to interact with the model, easily change the input parameters and observe how the results vary. This will allow the medical team to take more accurate decisions on the drug therapy to be proposed or on the timely planning of the surgery.