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Research

 

Development of Magnetic Resonance techniques
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  • Development and improvement of magnetic resonance imaging and spectroscopy techniques for clinical diagnosis and for use in physiological studies.
     
  • Development, testing and validation of methods providing quantitative analysis of tissue properties (biomarkers).

Magnetic resonance techniques have the potential to provide quantitative assessment of various tissue properties (as chemical composition, restriction of diffusion, relaxation times, blood perfusion) by use of suitable signal recording and post-processing strategies.

 

Our group aims at improved non-invasive assessment of tissue properties by adaptation of MR imaging techniques (sequences) and measuring parameters under the given circumstances (body area, breathing and cardiac motion, measuring time available). Studies on phantoms, volunteers and finally patients are conducted for these optimization procedures, and suitable correction methods are developed and implemented in order to compensate for adverse effects. For analysis of tissue properties in cohort studies modern automatic segmentation strategies are included and combined with algorithms for assessment of local tissue characteristics in the image date sets.


In addition to MR imaging dedicated spectroscopic methods are used for measuring metabolites with low concentrations in various tissues.

Automatic Analysis of Imaging Data
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  • Automatic analysis of medical imaging data sets with segmentation and volumetric assessment of organs and anatomical structures.
     
  • Use of texture analysis for tissue characterization.

In cohort studies large highly resolved whole-body MRI data sets are recorded in a high number of participants. Assessment of the anatomical arrangement and volumes of various tissues provides indications for the potential development of diseases. For example, subjects with a large visceral fat volume show a clearly increased risk for developing cardiovascular disease or diabetes. Automatic segmentation and evaluation procedures clearly support related studies.


In collaboration with the Institute for Signal Processing and System Theory at the University of Stuttgart (Prof. Bin Yang) conventional and advanced machine learning strategies for automatical segmentation and texture analysis are developed and tested.


Furthermore, MRI techniques are adapted to requirements of modern automatic evaluation tools regarding contrast, spatial resolution, and coil arrangement in ordner to obtain reliable and reproducible results.

More Informations
  • about Development of MR Techniques (PDF-file)
  • about MR Phenotyping in Diabetes Research (PDF-file)
 

 

Last updated: 10.04.2018






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