Research within KHeaT

Image Analysis

Dynamic contrast enhanced MRI (DCE-MRI) of the lung is used in clinical practice to assess the lung function of patients. We develop a method for quantifying the perfusion parameters and abnormalities based on mathematical methods. Main research points include image segmentation, image registration, image resolution improvement and breathing motion correction.

Application of Machine Learning methods to medical image analysis tasks, which include event detection, image segmentation and motion modelling in the area of lung MRI.

In this context, we also investigate the management of research data in radiology related but not restricted to Machine Learning. We use and adapt the in-house developed tool Kadi4Mat, which was developed for managing research data in the material sciences, for the creation of FAIR research data and producing reproducible workflows for medical image analysis tasks.


Membrane Technology

The demand for rapid and easy-to-use medical diagnostic devices, referred to as Point-of-Care-Testing (POCT), has increased over the last few years. Especially for lateral flow assays (LFAs), the advantages of POCT-devices are evident due to their user-friendliness and cost-efficiency. The working principle of LFAs is based on the capillary-driven liquid transport of a fluid through highly porous and open-pored polymeric membranes to a reaction zone (test and control line). 

In order to improve the sensitivity of the test, our research focuses on gaining fundamental knowledge about the influence of microstructure morphology on fluid propagation in the reaction zone. The steps to achieve this target are creating the digital twins of the porous microstructure, extracting effective structure properties, and linking them with the resulting flow behavior using data science approaches.

Particularly during major pandemics, LFAs allow controlling the spread of disease agents and thus, obviously contribute to global health.


Janus particles and emulsion


  •     Identify the key parameters for emulsion and Janus droplet formation


  •     Developing a numerical model (phase-field model) for emulsion and Janus particles
  •     Addressing the kinetic process of phase separation for the formation of Janus particles
  •     Investigating the effect of diffusion and convection on the development of Janus particles and emulsion


Digital twin for droplets evaporation on patterned substrate


  •     Create a digital twin of Droplet Microarray (DMA) by modeling the evaporation process
  •     Identify an optimal condition for culturing of cells and sample preparation on DMA


  •     Proposing a theoretical model for describing the quasi-equilibrium shape of a droplet on a hydrophilic spot
  •     Developing a 3D+t model (phase-field model) for droplet evaporation on Droplet Microarray (DMA) platform
  •     Identifying and investigating key parameters affecting the droplet evaporation on DMA