Studentenprojekte
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
Schlagwörter
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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Publiziert seit: 2025-01-17 , Frühester Start: 2020-09-01
Organisation(en) Cardiovascular Magnetic Resonance
Host(s) Buoso Stefano , Kozerke Sebastian, Prof
Themen 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.
Schlagwörter
Generative models, deep learning, phantoms, variational autoencoders, cardiac mechanics, cardiac function, simulation
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Master Thesis
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Publiziert seit: 2025-01-17 , Frühester Start: 2023-07-01
Organisation(en) Cardiovascular Magnetic Resonance
Host(s) Buoso Stefano , Kozerke Sebastian, Prof
Themen Information, Computing and Communication Sciences , Engineering and Technology
Cardiac Muscle Compartment Modelling for Finite Element Diffusion Simulation
The project aims to develop a compartment model for the cardiac muscle including the four major compartments (myocytes, mural cells, collagen and blood vessels). This model allows to simulated diffusion by solving local partial differential equations (PDE) with finite elements. This simulation approach has been established in diffusion tensor imaging (DTI) for the brain. You will be working on translating it to cardiac DTI.
Schlagwörter
mesh modelling, FE simulation, MRI, cardiac diffusion tensor imaging
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Semester Project , Master Thesis
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Publiziert seit: 2024-12-03 , Frühester Start: 2025-02-16 , Spätestes Ende: 2025-09-30
Organisation(en) Cardiovascular Magnetic Resonance
Host(s) Haltmeier Sandra
Themen Engineering and Technology , Biology
Cardiac Diffusion Tensor Imaging (cDTI) Inference on Digital Twins
The project aims to utilize respiratory motion to estimate sample points between slices and thus increase spatial coverage for cardiac diffusion tensor imaging (cDTI). By using the respiratory navigator data, you will map in-vivo cDTI data to a 3D digital twin mesh and implement a tensor estimation to estimate sample points between slices based on spatial smoothness regularization. You then perform an accuracy evaluation on simulated data.
Schlagwörter
cardiac diffusion tensor imaging, digital twins, respiratory motion, MRI, magnetic resonance imaging, cardiac imaging
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Semester Project , Bachelor Thesis , Master Thesis
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Publiziert seit: 2024-12-03 , Frühester Start: 2025-02-01 , Spätestes Ende: 2025-08-31
Organisation(en) Cardiovascular Magnetic Resonance
Host(s) Haltmeier Sandra
Themen Medical and Health Sciences , Engineering and Technology , Physics
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.
Schlagwörter
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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Publiziert seit: 2024-11-18 , Frühester Start: 2024-08-02 , Spätestes Ende: 2024-12-20
Bewerbungen eingeschränkt auf ETH Zurich , EXCITE Zurich
Organisation(en) Cardiovascular Magnetic Resonance
Host(s) Wolkerstorfer Gloria
Themen Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Digital twinning with physics-informed graph neural networks
The aim of this project is to develop an approach based on physics-based graph neural networks to generate digital twins from PC-MRI data.
Schlagwörter
Physics informed neural networks, graph neural networks, digital twins, blood flow, brain, circulatory system, AI, biophysical
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Master Thesis
Description
Goal
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Publiziert seit: 2024-10-05 , Frühester Start: 2023-11-30
Organisation(en) Cardiovascular Magnetic Resonance
Host(s) Buoso Stefano , Kozerke Sebastian, Prof
Themen Information, Computing and Communication Sciences , Engineering and Technology