Teaching

Teaching

Current ongoing and previous thesis

2024

  • MA

    Enhancing MR Downstream Task Performance by incorporating tabular data using a CLIP-based approach

  • MA

    Segmentation of Medical Images using Connectivity Masks as Input Data

  • MA

    Generative models for semantic map to MR image translation

  • MA

    Joint groupwise non-rigid registration and segmentation for accelerated cardiac cine MR Images

2023

  • MA

    Extending the Mutual Information Minimization Model with multiple confounders and classes

  • FA

    Deep learning based image quality assessment

  • MA

    Enhancing Classification Performance in Imbalanced Datasets: A CLIP-Based Approach with Metadata and MR Images

  • MA

    Does B-Cos Models improve the interpretability of deep-learning based medical image analysis?

  • MA

    Software algorithms for Endoscopic imaging

  • MA

    Automatic segmentation for multiparametric magnetic resonance images

2022

  • MA

    Hyperparameter optimization for MR image reconstruction architectures based on uncertainty estimation

  • BA

    Deep learning based locally low-rank approximation

  • MA

    Attention-based motion detection in epidemiological cohorts

  • MA

    Accelerating cardiac MR by exploiting spatio-temporal information

  • MA

    Self-supervised image registration for renal MRI

  • MA

    Robust MRI motion detection in varying environments

  • MA

    Uncertainty quantification for deep learning MR image reconstruction

2021

  • MA

    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

    Spatiotemporal Through-plane Super-resolution Networks for Isotropic Cardiac Cine MRI Investigated in the UK Biobank

2020

  • 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

Potential Bachelor/Research/Master thesis

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
In reconstruction step:
  • Conventional and deep-learning based image reconstruction: Inclusion of multi-parametric and motion information, high-dimensional data processing, model-based reconstructions
  • Non-rigid motion estimation and correction: Deep-learning based image registration and motion correction
  • Sensor fusion to map imaging states to external surrogate signals
In post-processing step:
  • Convolutional neural networks for MR image artifact localization and quantification
  • Generative models for image-to-image translation
  • Semantic segmentation of organs and tissues
  • Automatic quality control measures
  • Biomarker feature extraction
In analysis step:
  • Treatment response predicition
  • Combination of imaging and non-imaging data
  • Causal discovery
  • Biological age estimation

Prerequisites

  • Highly motivated, independent and structured way of working
  • Interest in machine learning, deep learning and signal processing
  • Good German and/or English skills (spoken and written)
  • Programming expertise in Python is beneficial
  • Knowledge about medical imaging is beneficial

Zertifikate und Verbände

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