Towards parametric estimation and efficient hemodynamics simulations of cardiac valves
| PhD ceremony: | Ms G. (Giorgia) G. Pase |
| When: | September 16, 2025 |
| Start: | 11:00 |
| Supervisors: | C.A. (Cristóbal) Bertoglio, Prof, prof. dr. ir. R.W.C.P. (Roel) Verstappen |
| Where: | Academy building RUG / Student Information & Administration |
| Faculty: | Science and Engineering |
Aortic stenosis, a narrowing of the heart’s aortic valve, can severely restrict blood flow and lead to life-threatening complications. Accurate knowledge of valve geometry and dynamics is essential for diagnosis, treatment planning, and the design of medical devices. However, while modern imaging techniques such as MRI and ultrasound can capture blood flow, they often cannot fully resolve the valve’s intricate structure without invasive methods like CT scans.
In her thesis, Giorgia Pase develops computational strategies to bridge that gap, contributing to the broader goal of creating cardiovascular “digital twins” — personalised computer models of a patient’s heart that integrate clinical data with physics-based simulations.
First, Pase designed a new parametric model of the aortic valve to represent both healthy and stenotic cases, accounting for asymmetry between leaflets. Validated on ten patient-specific cases, it produced realistic hemodynamic simulations and reproduced the expected relationship between pressure gradients and peak velocity.
Second, Pase addresses the inverse problem of estimating valve geometry from flow measurements. She tested a method based on the Reduced Order Unscented Kalman Filter on a simplified case, successfully reconstructing shapes in data-rich regions while highlighting challenges in parameter identifiability.
Finally, Pase proposed two novel fractional-step solvers to make simulations more computationally efficient without sacrificing accuracy, offering an alternative to traditional monolithic methods.
By integrating geometric modelling, inverse estimation, and efficient numerical solvers, this research advances the feasibility of personalised, non-invasive aortic valve reconstruction. These developments represent a step toward faster and more accurate cardiovascular diagnostics through digital twin technology.