Available Bachelor/research/Master thesisSome 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
×The inclusion of artifical intelligence (AI) into medical data processing can help to improve performance by (but not limited to) increasing precision, boosting quality of service, easing processing and reducing computational times. It enables to develop patient-centered workflows with personalized treatments that include: data processing from multiple imaging modalities acquired with multi-parametric imaging sequences, advanced reconstruction, post-processing and anylsis techniques.
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 topicsThe 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.
×Patient, respiratory and cardiac movements produce motion artefacts in the final image which reduce the quality and impair reliable diagnosis. The task is to accurately resolve, detect and correct the movements in acquisition, reconstruction and post-processing. High spatial-and temporal-resolved MR images need to beacquired over several respiratory and cardiac cycles under free movement conditions. Motion-resolved images are then reconstructed under the consideration of a derived motion model and surrogate signals guiding the motion model. Motion models are estimated by image registrations. In a retrospective correction, images affected by motion can be further corrected.
Applied methods include: Deep-learning based reconstruction, optical-flow and deep-learning image registration, Generative adversarial networks, sensor fusion
×Magnetic resonance (MR) imaging plays an important role in medical diagnostic imaging due to its high soft-tissue contrast and its non-invasive acquisition. One major drawback, however, is often the relatively long examination time. Therefore, acceleration of MR data acquisition is of great interest. One way to achieve this is to reduce the number of phase-encoding steps for a fixed resolution in Cartesian raw data acquisition. The resulting sub-Nyquist acceleration introduces aliasing artifacts in the image. Aliasing-free images can be recovered if (1) the images are compressible, i.e. can be sparsely represented in some transform domain, if (2) are randomly and incoherently sampled and if (3) a non-linear reconstruction is employed. Further consideration need to be taken for sharing of spatio-temporal-quantiative information and to handle the high-dimensional data processing efficiently.
Applied methods include: Compressed Sensing, deep-learning based image reconstruction, low-rank + sparse methods Magnetic resonance imaging is a flexible imaging modality, but can be prone to a variety of imaging artefacts which can be grouped as: patient-related, hardware/scanner-related and signal-processing-related. The artefacts can degrade the derived image quality and impair a reliable diagnosis. In order to provide a quantitative score, the human visual system needs to be mimicked which requires manual labeling by expert readers. Since expert labeling can be a time- and cost-intensive task, strategies for minimizing labeling effort need to be explored. The work deals with accurately detecting and correcting artifacts to provide an objective and reliable quality control measure during acquisition and/or post-processing.
Applied methods include: feature extraction, classification, reinforcement learning, active learning, semi-supervised learning
×Automatic segmentation of organs or tissue compartments in whole-body imaging is an important pre-requisite for any further analysis. The work deals with automatic detection of landmarks and segmentations for heart, abdominal organs (liver, spleen, pancreas, kidneys) and adiposse tissue compartments. Different convolutional neural network architectures are investigated together with a smooth integration into clinical practice. All thesis topics concern problem dependent aspects and appropriately selected algorithms:
- Classification: Bone and organ segmentation, tissue classification
- Regression: Anatomical structure localization
- Statistical analysis: Texture and shape analysis of specific regions and structures
- Extending and optimizing task dependent analysis pipelines
- Framework development: GUI, extending functionalities in Python
×The task of translating medical images between different domains has numerous useful applications. One example is the correction and restoration of artifact-corrupted images. Another potential application is the generation of novel image data between different modalities. The work focuses on the development and refinement of medical image translation frameworks and their applications - specifically, GAN-based MR motion correction, GAN-based PET attenuation correction and GAN-based image inpainting.
Applied methods include: variational autoencoders, 3D GANs
- 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
|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|