Student Projects

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) images are the gold standard for myocardial scar imaging. Classifying healthy and scarred tissue is an essential clinical step for treatment planning. In the last years, many automatic approaches based on deep learning have been proposed for the task. However, they require large annotated datasets, which are expensive and time-consuming to generate. To overcome this limitation, we are interested in generating a synthetic dataset for the task.

In this project, the student will enhance an existing pipeline to generate synthetic LGE images with realistic background textures and details. These synthetic images will then be used to train and test models for myocardium and scar segmentation. Specifically, the segmentation performance of nnU-Net on the synthetic images will be evaluated and compared to the performance of nnU-Net models trained on real, in-vivo LGE data. A clinical dataset, which includes LGE images and their corresponding segmentation masks, has been made available for this project.

Isabel Margolis, margolis@biomed.ee.ethz.ch

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for visualizing myocardial scars, which is crucial for classifying healthy versus scarred tissue and guiding treatment decisions. However, current methods are often unreliable due to intra- and inter-observer variability, lack of consensus, and limited standardization. To address these challenges, this project aims to generate synthetic data based on high-resolution, ground truth scar patterns, which can be used to train deep learning models for automatic and reliable scar segmentation.

In this project, the student will explore existing computational models and deepen their understanding of cardiac fibrosis and myocardial scar formation. The student will then extend an existing pipeline to simulate high-resolution scar patterns. Starting points for the literature review will include an overview of computational models of cardiac electrophysiology and fibrosis analysis based on histology or high-resolution CMR imaging techniques.

Isabel Margolis, margolis@biomed.ee.ethz.ch

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).

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.

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.

Supervisors: Dr. Stefano Buoso (buoso@biomed.ee.ethz.ch). To apply for this project please send a copy of your CV and transcripts of your Bachelor and Master studies.

Standardised performance assessment of image acquisition, reconstruction and processing methods is limited by the absence of images paired with ground truth reference values. Synthetic datasets could therefore be bridging this cap since they are associated with known parameters that could be used for computation of error metrics. However, these datasets are characterised by poor anatomical and functional variability which is usually limited to healthy conditions. With this project, we want to develop a generative model, based on deep-learning methods, for the generation of synthetic phantoms with sufficient anatomical variability to realistically represent population statistics. The project will be based on our recent work on generative models based and implemented in Pytorch.

The project aims at: - Building a generative model for left-ventricular 2D masks using convolutional variational autoencoders - Building a conditional generative model for the human torso tissues using convolutional variational autoencoders - Apply realistic tissue properties to the images using our texturizer network - Generating a dataset of representative cases including healthy and pathological left ventricles

Supervisors: Dr. Stefano Buoso (buoso@biomed.ee.ethz.ch). To apply for this project please send a copy of your CV and transcripts of your Bachelor and Master studies.

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. **We offer** - A warm start of the project with the state-of-the-art knowledge of the group in this field - A chance to connect state of the art research in MRI with modern ML tools

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.