Cardiovascular Magnetic Resonance

Open Opportunities

In-silico cardiac and cardiovascular modelling with physics informed neural networks ETH Zurich Cardiovascular Magnetic Resonance The aim of the project is to investigate the benefits, requirements and drawbacks of physics informed neural networks in the context of personalised cardiac and cardiovascular models Biomechanical Engineering, Clinical Engineering, Computation Theory and Mathematics, Fluidization and Fluid Mechanics, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Simulation and Modelling Master Thesis

Generation of synthetic cardiac phantoms for healthy and pathological anatomy and function using generative AI ETH Zurich Cardiovascular Magnetic Resonance The project focuses exploiting generative AI to build synthetic numerical phantom for cardiac anatomy and function suitable for representing population variability. Biomechanical Engineering, Information, Computing and Communication Sciences Master Thesis

Inference of Aortic Hemodynamic and Flow Features Using Physics-Informed Neural Networks ETH Zurich Cardiovascular Magnetic Resonance The aim of this project is to develop an automatic approach using physics-informed neural networks to infer hemodynamic parameters and flow quantities of in-silico aortic stenosis patients. Engineering and Technology, Information, Computing and Communication Sciences, Mathematical Sciences Bachelor Thesis, Master Thesis, Semester Project