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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
Cardiovascular Magnetic Resonance MR Imaging (MRI) allows for the acquisition of subject-specific anatomical, functional as well as microstructural data of the heart. In the last years, several frameworks based on machine learning have been proposed to classify, label and segment MRI cardiac data. In a second step, personalised in-silico models are generated to analyse and enrich he available information from MRI. Conventionally, in these models, discretise the partial differential equations describing cardiac mechanics, electrophysiology and haemodynamics are solved relying on Finite Elements or Finite Volumes. In this project, we want the explore the challenges and requirements to extend these neural networks frameworks to the generation of personalised in-silico models. Particularly, we are interested in physics informed neural networks.
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 (Tensorflow, Pytorch, Keras).
Cardiovascular Magnetic Resonance MR Imaging (MRI) allows for the acquisition of subject-specific anatomical, functional as well as microstructural data of the heart. In the last years, several frameworks based on machine learning have been proposed to classify, label and segment MRI cardiac data. In a second step, personalised in-silico models are generated to analyse and enrich he available information from MRI. Conventionally, in these models, discretise the partial differential equations describing cardiac mechanics, electrophysiology and haemodynamics are solved relying on Finite Elements or Finite Volumes. In this project, we want the explore the challenges and requirements to extend these neural networks frameworks to the generation of personalised in-silico models. Particularly, we are interested in physics informed neural networks. 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 (Tensorflow, Pytorch, Keras).
Our investigation has multiple objectives and can accommodate for the student’s interests. Please contact us if you are interested and would like to know more.
Potential projects include:
- Modelling of cardiac mechanics,
- Atlas based and reduced order modelling of cardiac mechanics,
- Simulation of blood flow in arteries and left ventricles,
- Reduced order modelling of blood flow,
- Inverse problems in cardiac mechanics and fluid dynamics,
- Generation of synthetic MRI images based on biophysical models and training of neural networks for functional evaluation of clinical images.
**Requisites**
Programming experience in Python is required. Familiarity with Tensorflow or Pythorch or Keras is a plus.
Our investigation has multiple objectives and can accommodate for the student’s interests. Please contact us if you are interested and would like to know more. Potential projects include:
- Modelling of cardiac mechanics,
- Atlas based and reduced order modelling of cardiac mechanics,
- Simulation of blood flow in arteries and left ventricles,
- Reduced order modelling of blood flow,
- Inverse problems in cardiac mechanics and fluid dynamics,
- Generation of synthetic MRI images based on biophysical models and training of neural networks for functional evaluation of clinical images.
**Requisites** Programming experience in Python is required. Familiarity with Tensorflow or Pythorch or Keras is a plus.
Supervisors: Dr. Stefano Buoso (buoso@biomed.ee.ethz.ch); Dr. Thomas Joyce (joyce@biomed.ee.ethz.ch); Prof. Dr. Sebastian Kozerke (kozerke@biomed.ee.ethz.ch). To apply for this project please send a copy of your CV and transcripts of your Bachelor and Master studies.
Supervisors: Dr. Stefano Buoso (buoso@biomed.ee.ethz.ch); Dr. Thomas Joyce (joyce@biomed.ee.ethz.ch); Prof. Dr. Sebastian Kozerke (kozerke@biomed.ee.ethz.ch). To apply for this project please send a copy of your CV and transcripts of your Bachelor and Master studies.
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