Aim

The aim of this task is to provide methods for the processing of all image material involved in the project. This involves primarily the compensation for patient motion, the correction of image distortion, the segmentation of relevant structures in the images, and the classification of tissues inside these structures.

Approach

Since multi-parametric quantitative imaging consists of the execution of several image acquisition sequences with often considerable duration, the patient or body tissues may move during or in between the acquisitions, so that the anatomy is no longer spatially registered between the resulting image volumes. For multi-parametric image processing and interpretation, it is vital that the image volumes overlap exactly voxel-by-voxel. Therefore image registration is the first task for this DR THERAPAT topic. Distortion that may be present in the images, e.g. due to the specific imaging process, must be corrected for.
Once the image volumes are aligned, they need to be understood. This involves recognition or segmentation of organs and, for radiotherapy with dose painting, the segmentation and classification of different tumor tissues so that radiation doses can be differentiated for the different tissue classes during the radiobiology modeling stage. Model-based segmentation and classification approaches will be investigated.

Expected outcome

The task will deliver a dedicated set of image processing software components, which will be integrated into the DR THERAPAT demonstrator (see also task ‚ÄúPrototype Implementation‚ÄĚ).