Dynamic images, i.e., 2D or 3D images acquired over time, are used in biology and medicine in order to capture rapid kinetic processes in organisms in vivo and can be derived with a variety of technologies, including, amongst others, fluorescence microscopy and magnetic resonance imaging (MRI). From a statistical point-of-view dynamic images - independent from the modalities they have been acquired with - share a similar data structure. The signal time curve in each voxel can be described by kinetic models based on the biological processes in the organism. Biological models for dynamic images are often oversimplified to ease parameter fitting. The aim of this project is to develop and to apply advanced spatial statistical models for the analysis of dynamic images. We use Bayesian inference to allow for robust parameter estimation in more realistic biological models. Criteria for the choice between competing local kinetic models will be developed. In contrast to existing model choice, we will account for the fact that local kinetic time curves are not independent. By using spatial prior information, more robust estimators and, additionally, information about the spatial structure will be obtained. In addition, we will develop methods to analyze multiple dynamic images simultaneously. The development of statistical methodology will mainly be driven by problems in two applications of dynamic images, Fluorescence Recovery After Photobleaching (FRAP) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI).
Coordinator(s): Prof. Dr. Volker Schmid
Staff: Julia Sommer, Martina Feilke