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Applicant:
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Professor Dr.-Ing. Bernhard Preim, Magdeburg
Otto-von-Guericke-Universität Magdeburg
Institut für Simulation und Graphik
Professor Dr.-Ing. Klaus-Dietz Tönnies, Magdeburg
Otto-von-Guericke-Universität Magdeburg
Institut für Simulation und Graphik
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Summary:
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Spatial and temporal resolution of tomographic medical image data (CT, MRI, etc.) being acquired in medical diagnostics and clinical studies have increased substantially and will further increase. Particularly for dynamic image data, the evaluation software does not sufficiently exploit the rich information.
A framework shall be developed that combines image interpretation techniques with the visual analysis of 4D dynamic medical image data. Perfusion data is an important and representative example for dynamic medical image data. These data are acquired, e.g., in ischemic stroke, cardiac, and tumor diagnosis. A multi-dimensional space of perfusion parameters needs to be explored to perform a reliable diagnosis.
For the first time, adaptive model-based segmentation techniques will be developed to delineate regions of interest in these 4D data sets. Such a visually supported analysis has several advantages:
• Implicit training lets the user adapt the tool for specializing it to selected problems in perfusion diagnosis.
• An efficient general solution is provided that might be adapted according to the specific imaging device, the imaging sequence, or the type of contrast agent administration.
• Interpretation tools can be extended to similar analysis problems, e.g., fMRI data evaluation.
Techniques from cluster analysis, dimension reduction, and image segmentation will be used to extract features for visualization. 3D visualization techniques will be refined and adapted to the peculiarities of high resolution perfusion data. Data exploration will support researching physicians and medical physicists to assess the influence of image acquisition parameters on the expressiveness of perfusion parameters and combinations thereof.
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