Teaching
Available Bachelor/research/Master thesis
Some of the topics are in collaboration with the Institute of Signal Processing and System Theory, University of Stuttgart and the Max Planck Institute for Intelligent Systems, Empirical Inference, Tübingen.The following section gives an overview of the potential topics.
Overview: AI-assisted data processing

In acquisition step:
- Sequence development for multi-parametric and motion-resolved MRI
- Patient-adaptive sampling optimization including online feedback according to movement cycle, SNR, parametric information
- Monitor imaging state by external sensors (Microsoft Kinect camera, respiratory belt, ...) to match e.g. to a motion model
- Conventional and deep-learning based image reconstruction: Inclusion of multi-parametric and motion information, high-dimensional data processing
- Non-rigid motion estimation and correction: Deep-learning based image registration and motion correction
- Sensor fusion to map imaging states to external surrogate signals
- Framework development for Gadgetron
- Convolutional neural networks for MR image artifact localization and quantification
- Generative adversarial networks (GANs) for MR motion correction
- Semantic segmentation of organs and tissues
- Automatic quality control measures
- Biomarker feature extraction
- Framework development for NORA browser
- Treatment response predicition
- Combination of imaging and non-imaging data
- GANs for image synthesis
- Biological age estimation
Proposed topics
The proposed topics here are a selection of the current research projects. If you are interested in any other topic, please refer to the Github projects or software and code documentation and contact one of the employees directly.Motion correction
Contact: ;
- deep-learning based reconstruction
- random walks
- optical-flow and deep-learning image registration
- generative adversarial networks
- sensor fusion
Image reconstruction
Contact: ;
- Compressed Sensing
- deep-learning based image reconstruction
- model-based reconstruction (motion, parametric)
- low-rank + sparse methods
- super-resolution
Image quality assessment and control
Contact:- feature extraction
- classification
- reinforcement learning
- active learning
- semi-supervised learning
Semantic segmentation
Contact:
Automatic segmentation of organs or tissue compartments in whole-body imaging is an important pre-requisite for any further analysis. We develop Deep learning based algorithms for the automatic detection of landmarks and the segmentation of organs, tissue compartiments or tumors. Our work focuses on the development and transfer of computer vision and machine learning techniques to achieve accurate results with a small number of labels and to enable the generalization to new environments.
- Self supervision and contrastive learning
- Weakly- and semi supervised learning for computer vision tasks
- Attenion mechanisms and Graph Neural Networks
- Domain adaptation and domain generalization
- Geometrical and statistical shape analysis
Probabilistic machine learning
Contact: ; ; ;A variety of problems in medical imaging can be traced back to elementary problems from statistics and machine learning. The goal of our research is to relate fundamental theoretical insights to real-world problems. Our key topics are the quantification of uncertainty and the question of how causal relationships in observational data can be identified and leveraged in machine learning applications.
- Uncertainty and calibration for DL
- Generative models, identifiability and approximative inference
- Disentanglement and (causal) representation learning
- Causal discovery with hidden confounders
- Invariant mechanisms for domain generalization
Generative models
Contact:
- variational autoencoders
- generative adversarial networks
- uncertainty estimation
Prerequisites
- Highly motivated, independent and structured way of working
- Interest in machine learning, deep learning and signal processing
- Studies in the field of electrical engineering, informatics or Medizintechnik
- Good German and/or English skills (spoken and written)
- Programming expertise in Python is beneficial
Current ongoing and previous thesis
2022
Type | Topic |
---|---|
MA | Uncertainty estimation via deep ensembling for MR image reconstruction |
BA | Deep learning based locally low-rank approximation |
MA | Self-assisted priors for motion artifact detection and correction |
2021
Type | Topic |
---|---|
FA | 3D Generative models for MR image super resolution |
BA | DL-based motion-corrected reconstruction: an evaluation study |
MA | Cardiac MR image reconstruction levearaging spatio-temporal redundancies |
MA | Motion-compensated image reconstruction using generative networks |
MA | MR motion artefact detection and correction in image and k-space |
MA | Temporal-aware super resolution for cardiac CINE MR |
MA | Efficient long-term and short-term MR image registration |
MA | Accelerating cardiac MR by exploiting spatio-temporal information |
2020
Type | Topic |
---|---|
MA | Clinical feasible pipeline for semantic MR segmentation |
MA | Clinical feasible pipeline for motion artifact detection and correction |
MA | LAP-Net: Deep learning-based non-rigid registration in k-space for MR imaging |
MA | Prediction of response to immunotherapy and overall survival rate in temporal staging of melanoma patients with multi-modal hybrid imaging |
FA | Automatic lesion segmentation and staging in a cohort of melanoma patients acquired with multi-modal hybrid imaging |
BA | Intelligent brushes for automatic segmentation and detection in multi-modality imaging |
MA | DL-based motion-corrected reconstruction of time-resolved MRI |
MA | Semantic Segmentation for renal MRI |
FA | Evaluation and optimization of non-rigid registration in k-space |
MA | Exploiting spatio-temporal redundancies for high-dimensional deep-learning based image reconstruction |
FA | Plug-and-play priors for MR motion artifact detection and correction |
FA | Investigation on the efficacy of plug and play priors and unrolled physics-based deep-learning MR image reconstruction |