To assess this risk, it is essential to create the most accurate possible 3D model of the vessel. Using such models, biomechanical experts can evaluate mechanical stress in the aortic wall and thus estimate the likelihood of rupture. The most common inputs for 3D reconstructions are imaging techniques such as computed tomography (CT) or magnetic resonance imaging (MRI). However, these images typically contain significant noise, and tissue boundaries are not always clearly defined. As a result, the generated 3D models are often inaccurate and require time-consuming manual corrections.

To address this, researchers employ probabilistic mathematical models that can better handle uncertainty in the data while incorporating prior knowledge of vascular anatomy.
“Mathematical modelling makes it possible to combine imperfect information from CT scans with expert knowledge about the anatomy of the structures being studied, such as the natural smoothness of vessel walls. The model parameters are then determined using data from a specific patient, resulting in an accurate and realistic three-dimensional model of the given anatomical region,” explains Matěj Mazůrek from the Department of Applied Mathematics at the Faculty of Electrical Engineering and Computer Science, VSB – Technical University of Ostrava.

The resulting 3D model of the vessel will be used by experts from the team of Associate Professor Stanislav Polzer at the Department of Applied Mechanics, Faculty of Mechanical Engineering, as a basis for biomechanical calculations, simulations, and subsequent assessment of aneurysm rupture risk.

The research also demonstrates the practical application of knowledge gained in the Computational and Applied Mathematics study programme.