Cardiovascular Magnetic Resonance

Open Opportunities

Analysis of Cerebral Flow using CFD and Comparison to In-vivo Data ETH Zurich Cardiovascular Magnetic Resonance The aim of this project is to perform high resolution CFD simulations of pathological patient-specific cerebral vasculatures to analyze hemodynamic flow parameters and compare with In-vivo MRI data. Biomechanical Engineering, Mechanical Engineering, Simulation and Modelling Master Thesis, Semester Project

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

Cardiac Muscle Compartment Modelling for Finite Element Diffusion Simulation ETH Zurich Cardiovascular Magnetic Resonance 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. Biomaterials, Biomechanical Engineering, Mechanical Engineering, Modeling and Simulation Master Thesis, Semester Project

Cardiac Diffusion Tensor Imaging (cDTI) Inference on Digital Twins ETH Zurich Cardiovascular Magnetic Resonance 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. Biomedical Engineering, Human Biophysics, Medical Physics Bachelor Thesis, Master Thesis, Semester Project

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

Generation of Synthetic Pathological Magnetic Resonance Images using Generative AI ETH Zurich Cardiovascular Magnetic Resonance The aim of the project is to generate synthetic LGE CMR images from ground truth segmentation masks using a conditional GAN. Biomedical Engineering, Computer Vision, Image Processing, Simulation and Modelling Bachelor Thesis, Semester Project

Simulating High-Resolution Myocardial Scar Patterns for Synthetic Cardiac MRI Generation ETH Zurich Cardiovascular Magnetic Resonance This project aims to generate synthetic LGE CMR images by simulating high-resolution myocardial scar patterns. The student will extend an existing pipeline and use computational modeling techniques to improve the accuracy and realism of the scar pattern simulations. Artificial Intelligence and Signal and Image Processing, Biomedical Engineering, Modeling and Simulation Bachelor Thesis, Master Thesis, Semester Project

Digital twinning with physics-informed graph neural networks ETH Zurich Cardiovascular Magnetic Resonance 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. Artificial Intelligence and Signal and Image Processing, Biomedical Engineering, Fluidization and Fluid Mechanics, Turbulent Flows Master Thesis