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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.
Keywords: Aortic Stenosis, Physics-informed neural network, in-silico analyis, digital twins, Aorta, AI, medical imaging, machine learning
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 hemodynamic and flow analysis are a fundamental step in diagnosing and monitoring disease progression. However, limited data availability and lack of ground truth information prohibit the development of generalized and robust models.
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 hemodynamic and flow analysis are a fundamental step in diagnosing and monitoring disease progression. However, limited data availability and lack of ground truth information prohibit the development of generalized and robust models.
The aim of this project is to develop a method using physics-informed neural networks to learn aortic flow behaviour and identify aortic hemodynamic properties, which hold important information used for the assessment of aortic stenosis.
The student will develop and expand existing physics-informed neural network approaches, as proposed in the literature, to analyse gemetrical and velocity field properties from in-silico generated aortae. Synthetic and subsequently real data will be used to test the approach. The project will be carried out in Python and make use of open-source libraries such as Pytorch and Pyvista.
The aim of this project is to develop a method using physics-informed neural networks to learn aortic flow behaviour and identify aortic hemodynamic properties, which hold important information used for the assessment of aortic stenosis.
The student will develop and expand existing physics-informed neural network approaches, as proposed in the literature, to analyse gemetrical and velocity field properties from in-silico generated aortae. Synthetic and subsequently real data will be used to test the approach. The project will be carried out in Python and make use of open-source libraries such as Pytorch and Pyvista.
Gloria Wolkerstorfer (wolkerstorfer@biomed.ee.ethz.ch), 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.
Gloria Wolkerstorfer (wolkerstorfer@biomed.ee.ethz.ch), 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.