Phase Contrast Cardiovascular Magnetic Resonance MR Imaging (PC-MRI) allows for the subject specific quantitative assessment of blood flow. As abnormal flow patterns are related to the evolution of cardiovascular disease, automatic tools for flow analysis are a fundamental step for the clinical adoption of PC-MRI data in clinical practice.
Before extracting meaningful biomarkers from patients’ flow measurements, data needs to be denoised, augmented and filtered. Digital twins, which are in-silico replica of patients, could be used for this task, as they provide a computational framework suitable for data processing.
Phase Contrast Cardiovascular Magnetic Resonance MR Imaging (PC-MRI) allows for the subject specific quantitative assessment of blood flow. As abnormal flow patterns are related to the evolution of cardiovascular disease, automatic tools for flow analysis are a fundamental step for the clinical adoption of PC-MRI data in clinical practice. Before extracting meaningful biomarkers from patients’ flow measurements, data needs to be denoised, augmented and filtered. Digital twins, which are in-silico replica of patients, could be used for this task, as they provide a computational framework suitable for data processing.
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. These will combine the flexibility of graph neural networks, which could easily adapt to each patient, with physical constraints defined by physiological priors injected to the network. The investigation could target blood flow in the aorta, in carotids or in the brain
The student will develop and expand existing graph neural networks proposed in the literature to work with flow measurements from PC-MRI. Either real or synthetic data will be used to test the approach. The project will make use of our existing numerical tools based on open-source libraries for Finite Elements (FEniCS), Finite Volumes (openFOAM) and machine learning (Pytorch).
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. These will combine the flexibility of graph neural networks, which could easily adapt to each patient, with physical constraints defined by physiological priors injected to the network. The investigation could target blood flow in the aorta, in carotids or in the brain
The student will develop and expand existing graph neural networks proposed in the literature to work with flow measurements from PC-MRI. Either real or synthetic data will be used to test the approach. The project will make use of our existing numerical tools based on open-source libraries for Finite Elements (FEniCS), Finite Volumes (openFOAM) and machine learning (Pytorch).
Supervisors: Dr. Stefano Buoso (buoso@biomed.ee.ethz.ch). To apply for this project please email a copy of your CV and transcripts of your Bachelor and/or Master studies.
Supervisors: Dr. Stefano Buoso (buoso@biomed.ee.ethz.ch). To apply for this project please email a copy of your CV and transcripts of your Bachelor and/or Master studies.