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Generation of synthetic cardiac phantoms for healthy and pathological anatomy and function using generative AI
The project focuses exploiting generative AI to build synthetic numerical phantom for cardiac anatomy and function suitable for representing population variability.
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.
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
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.
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.