%0 Generic
%A Debus, Charlotte Christiane
%D 2016
%F heidok:21611
%R 10.11588/heidok.00021611
%T Integrative multimodal image analysis  using physical models for characterization  of brain tumors in radiotherapy
%U https://archiv.ub.uni-heidelberg.de/volltextserver/21611/
%X Therapy failure with subsequent tumor progress is a common problem in radiotherapy of  high grade glioma. Definition of treatment volumes with CT and MRI is limited due to  uncertainties concerning tumor outlines. The goal of the presented work was to enable  assessment of tumor physiology and prediction of progression patterns using multi-modal  image analysis and thus, improve target delineation. Physiological imaging modalities, such  as 18F-FET PET, diffusion and perfusion MRI were used to predict recurrence patterns.  The Medical Imaging Interaction ToolKit together with own software implementation  enabled side-by-side evaluation of all image modalities. These included tools for PET  analysis and a module for voxel wise fitting of dynamic data with pharmacokinetic models.  Robustness and accuracy of parameter estimates were studied on synthetic perfusion data.  Parameter feasibility for progression prediction was investigated on DCE MRI and 18F-FET  PET data. Using the developed software tools, a pipeline for prediction of tumor progression  patterns based on multi-modal image classification with a random forest machine  learning algorithm was established. Exemplary prediction analysis was applied on a small  patient set for illustration of workflow functionality and classification results.