A surface-matching technique for robot-assisted registration

Surface matching for high-accuracy registration of the lateral skull base

PURPOSE

The accuracy achievable when utilizing image guidance depends to a large extent on the accuracy with which the patient can be registered to preoperative image data. This work proposes a method for the registration of the temporal bone based on surface matching and investigates the achievable accuracy of the technique.

METHODS

Fourteen human temporal bones were utilized for evaluation; incisions were made, fiducial screws were implanted to act as a ground truth, and imaging was performed. The positions of the fiducials and surface of the mastoid were extracted from image data and reference positions defined at the round window and the mastoid surface. The surface of the bone was then digitized using a tracked pointer within the region exposed by the incisions and the physical and image point clouds registered, with the result compared to the fiducial-based registration.

RESULTS

Results of one case were excluded due to a problem with the ground truth registration. In the remaining cases an accuracy of [Formula: see text] and [Formula: see text] mm was observed relative to the ground truth at the surface of the mastoid and round window, respectively.

CONCLUSIONS

A technique for the registration of the temporal bone was proposed, based on surface matching after exposure of the mastoid surface, and evaluated on human temporal bone specimens. The results reveal that high-accuracy patient-to-image registration is possible without the use of fiducial screws.

A perspective on robotic assistance for knee arthroplasty

Knee arthroplasty is used to treat patients with degenerative joint disease of the knee to reduce pain and restore the function of the joint. Although patient outcomes are generally quite good, there are still a number of patients that are dissatisfied with their procedures. Aside from implant design which has largely become standard, surgical technique is one of the main factors that determine clinical results. Therefore, a lot of effort has gone into improving surgical technique including the use of computer-aided surgery. The latest generation of orthopedic surgical tools involves the use of robotics to enhance the surgeons' abilities to install implants more precisely and consistently. This review presents an evolution of robot-assisted surgical systems for knee replacement with an emphasis on the clinical results available in the literature. Ever since various robotic-assistance systems were developed and used clinically worldwide, studies have demonstrated that these systems are as safe as and more accurate than conventional methods of manual implantation. Robotic surgical assistance will likely result in improved surgical technique and improved clinical results.

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 pre-registration 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 pre-registration 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.