Tobias Sielhorst's Dissertation Defense on 'New Methods for Medical Augmented Reality'

The increasing amount of imaging data in the operating room offers new possibilities for surgeons. Preoperative data like Computed Tomography and Magnetic Resonance Imaging allow for detailed anatomical information. Recently, intraoperative imaging like X-ray C-arms and ultrasound have become increasingly accessible in the OR and provide real-time anatomical images. Also, functional imaging such as PET and fMRI is accessible in a growing number of institutions. Corresponding intraoperative functional probes allow more efficient procedures to be suggested in the near future.

The growing amount of imaging data also increases the difficulty of finding the desired piece of information. Thus, efficient data representation becomes increasingly important to doctors for an efficient use of the data. Visualization in computer assisted surgical solutions has not coped with the recent developments and does still not provide solutions which allow surgeons to take full advantage of the above technology.

Augmented Reality is a technology that has potential to improve doctors' performance by effectively displaying spatial relations in medical image data, decreasing the distance between patient and data, and relating hand movements and image data in the same coordinate system.

After more than a decade of research in medical Augmented Reality, the technology seems to be sufficiently understood to create prototype systems for a showroom. However, it has not found its way into operating rooms.

This thesis addresses current issues of medical Augmented Reality regarding the right place, right time, and the right way of data representation. Its literature review provides extensive research about medical AR setups. The seconds chapter features a detailed overview on components that are necessary for a medical AR system and includes a new method for temporal registration in AR. The following chapter discusses how to integrate these components describing the developed software framework. The last chapter describes new methods for assessment of a medical AR system addressing latency measurement and dynamic error prediction. The assessment chapter concludes with validation steps that have been performed for the system that has been developed as a part of the project NARVIS.