Fast and accurate registration of cranial CT images with A-mode ultrasound

Purpose Within the CRANIO project, a navigation module based on preoperative computed tomography (CT) data was developed for Computer and Robot Assisted Neurosurgery. The approach followed for non-invasive user-interactive registration of cranial CT images with the physical operating space consists of surface-based registration following preregistration based on anatomical landmarks. Surface-based registration relies on bone surface points digitized transcutaneously by means of an optically tracked A-mode ultrasound (US) probe. As probe alignment and thus bone surface point digitization may be time-consuming, we investigated how to obtain high registration accuracy despite inaccurate preregistration and a limited number of digitized bone surface points. Furthermore, we aimed at efficient man-machine-interaction during the probe alignment process. Finally, we addressed the problem of registration plausibility estimation in our approach. Method We modified the Iterative Closest Point (ICP) algorithm, presented by Besl and McKay and frequently used for surface-based registration, such that it can escape from local minima of the cost function to be iteratively minimized. The random-based ICP (R-ICP) we developed is less influenced by the quality of the pre-registration as it can escape from local minima close to the starting point for iterative optimization in the 6D domain of rigid transformations. The R-ICP is also better suited to approximate the global minimum as it can escape from local minima in the vicinity of the global minimum, too. Furthermore, we developed both CT-less and CT-based probe alignment tools along with appropriate man-machine strategies for a more time-efficient palpation process. To improve registration reliability, we developed a simple plausibility test based on data readily available after registration. Results In a cadaver study, where we evaluated the R-ICP algorithm, the probe alignment tools, and the plausibility test, the R-ICP algorithm consistently outperformed the ICP algorithm. Almost no influence of the pre-registration on the final R-ICP registration accuracy could be observed. The probe alignment tools were judged to be useful and allowed for the digitization of 18 bone surface points within 2 min on average. The plausibility test was helpful to detect poor registration accuracy. Conclusion The R-ICP algorithm can provide high registration accuracy despite inaccurate pre-registration and a very limited number of data points. R-ICP registration was shown to be practical and robust versus the quality of the pre-registration. Time-efficiency of the cranial palpation process may be greatly increased and should encourage clinical acceptance.