Student Projects
In-silico cardiac and cardiovascular modelling with physics informed neural networks
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
Keywords
cardiac modelling, neural network, in-silico models, personalized medicine, reduced-order modelling, fluid dynamics, continuum mechanics, aortic flow
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Master Thesis
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Published since: 2025-01-17 , Earliest start: 2020-09-01
Organization Cardiovascular Magnetic Resonance
Hosts Buoso Stefano , Kozerke Sebastian, Prof
Topics Information, Computing and Communication Sciences , Engineering and Technology
Generation of synthetic cardiac phantoms for healthy and pathological anatomy and function using generative AI
The project focuses exploiting generative AI to build synthetic numerical phantom for cardiac anatomy and function suitable for representing population variability.
Keywords
Generative models, deep learning, phantoms, variational autoencoders, cardiac mechanics, cardiac function, simulation
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Master Thesis
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Published since: 2025-01-17 , Earliest start: 2023-07-01
Organization Cardiovascular Magnetic Resonance
Hosts Buoso Stefano , Kozerke Sebastian, Prof
Topics Information, Computing and Communication Sciences , Engineering and Technology
Inference of Aortic Hemodynamic and Flow Features Using Physics-Informed Neural Networks
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.
Keywords
Aortic Stenosis, Physics-informed neural network, in-silico analyis, digital twins, Aorta, AI, medical imaging, machine learning
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2024-11-18 , Earliest start: 2024-08-02 , Latest end: 2024-12-20
Applications limited to ETH Zurich , EXCITE Zurich
Organization Cardiovascular Magnetic Resonance
Hosts Wolkerstorfer Gloria
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology