EXCITE ZurichOpen OpportunitiesThis project aims at developing a machine learning approach (for example, using convolutional neural networks) for localizing and tracking anatomical landmarks from cardiac MR images. - Biomedical Engineering, Electrical and Electronic Engineering
- Master Thesis
| 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
| 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
| The project aims to modernize and improve the process of medical image registration, currently performed through a method known as pTV. Offering a unique combination of numerical programming and practical software implementation, this project promises visibility and application in the ever-evolving field of medical imaging technology. Suitable as a semester-long or master's project. - Computer Software, Medical and Health Sciences
- Bachelor Thesis, Course Project, ETH Zurich (ETHZ), Lab Practice, Master Thesis, Semester Project
| The aim of this project is to develop a camera-based solution for motion correction of cerebrovascular 4D flow MRI, including hardware development and (deep learning-based) data analysis. - Biomedical Engineering, Computer Vision
- Bachelor Thesis, Master Thesis
| 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
| In Flow MRI, image artifacts mainly result from cardiac and respiratory motion, causing blurring or ghosting. CINE imaging addresses cardiac motion by acquiring data throughout the cardiac cycle. To tackle respiratory motion, traditional methods involved measuring respiratory signals and accepting data within a limited respiratory motion range, at the cost of reduced scan efficiency and increased acquisition time. Newer approaches record data in a free breathing manner and use self-navigation to organize it into bins, improving efficiency and reducing acquisition time.
Low rank priors are a cutting-edge technique in dynamic MR image reconstruction, and recent research by Hoh et al. has shown that incorporating motion information into locally low rank (LLR) reconstruction (MI-LLR) between bins can improve reconstructions for free breathing 3D cardiac perfusion MRI.
The aim of this project is to investigate the benefit of using MI-LLR reconstructions on Flow data.
- Engineering and Technology, Information, Computing and Communication Sciences, Mathematical Sciences, Medical and Health Sciences, Physics
- Bachelor Thesis, Master Thesis, Semester Project
| 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
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