Deformation for image guided interventions using a three component tissue model

Abstract

In image guided neurosurgery it is necessary to align preoperative image data with the patient. The rigid body approximation is usually applied, but is often not valid due to tissue deformation. Most non-rigid registration algorithms, such as those used for atlas matching, provide a smooth deformation, which does not model the characteristics of different tissues accurately since, for example, bone will appear to deform. We suggest that a physically based model of tissue could provide a powerful tool for tracking tissue movement. Since the algorithm must ultimately run in real time, we have developed a simplified model of tissue deformation based on a three component system. Regions are labelled as either rigid, deformable or fluid. A novel strategy to avoid folding in the transformation is described. Our model was applied to MRI and CT data from a neurosurgery patient with epilepsy. The test data is limited and the current implementation is in 2D, but initial results are promising.