Student Projects

In clinical routine, 2D and 4D Flow MRI are typically performed with cardiac synchronization [1] using an electrocardiogram (ECG) with patch electrodes [2] to bin the data into cardiac phases. Since the ECG signal is distorted by the magneto-hydrodynamic effect [3] and may be compromised by the switching of the MRI gradient fields [4], robust gating can be challenging. Since the quality also depends on the positioning and skin contact of the patch electrodes, the setup process can be time consuming. To address these issues, alternative techniques have been developed such as self-gating techniques [5], pilot tone navigation [6] and contact finger pulse units [7]. Inspired by approaches using camera-based photoplethysmography (PPG) [8], which can be limited by frame rate and/or PPG signal-to-noise ratio, the use of a contactless fiber-optic PPG device on the forehead in a contactless fashion is proposed.

The project aims at: • Establishing a data quality baseline using contact-based forehead-PPG measurements. • Collecting data using a contactless PPG acquisition. • Collection of various PPG datasets: wavelength (green and red), skin type (Fitzpatrick scale), demographics (age, …). • Study PPG signal quality and apply classical signal processing and machine learning methods to extract triggers/gating signals from the PPG signal.

Sébastien Emery (emery@biomed.ee.ethz.ch), Marco Giordano (marco.giordano@pbl.ee.ethz.ch) , Dr. Tommaso Polonelli (Tommaso.polonelli@pbl.ee.ethz.ch), Prof. Dr. Sebastian Kozerke (kozerke@biomed.ee.ethz.ch)

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.