Einsatz von prä- und intraoperativer Bildgebung zur Unterstützung von Operationsplanung und -durchführung in der roboterassistierten Chirurgie

This dissertation describes the development of computer-aided processes, for processing medical images from different pre- and intraoperative imaging modalities. The main focus lies on supporting the execution of minimal invasive surgical procedures with robotic assistance systems. Those systems offer the potential to enhance the accuracy and the reproducibility of surgical procedures significantly. On the other hand, such systems will only become established, if they are easy and safe to use for operators. The needed special requirements for the software, used for planning and execution, are analyzed within this review and implemented in a proposal for solution.
The medical background for this review were two projects applied in practice. The first project describes the further development of a software for planning and execution of a spine surgical intervention, using a robotic assistance system. In the first step, the intraoperative imaging modality has to be registered with the medical image of the patient. Therefore, a procedure is developed, which uses markers that are detected by an optical stereo camera system as well as by the intraoperatively used x-ray system. In order to enable the surgeon to plan the intervention beforehand, preoperative images are used. To execute the preoperative planning, it has to be transferred to the actual patient position on the operating table. Therefore, a suitable method is implemented to match the preoperative image data with intraoperative images.
The second project addresses the operation of a new exible arm for minimal invasive surgery. The optical positioning system, required to operate such device safely, consists of an endoscopic camera that is monitored by a stereo camera system. The biggest challenge lies in the fast and precise detection of the exible arm using the images of the endoscopic camera. To determine the position of the exible arm, a suitable method has to be selected and implemented. With the aim of making the system exible and adaptable, algorithms that use machine learning are applied to solve the individual problem.