Rüdiger Marmulla

Laser-scan-based navigation in cranio-maxillofacial surgery

BACKGROUND In computer-assisted surgery, a correlation between a volume data set and the surgical site is required in order to localize the patients head on the operating table. Registration markers are commonly used for this procedure. However, the marker registration is associated with high logistics, since the markers have to be placed prior to data set acquisition and have to be kept in their position until the patient enters the operating room. This study deals with a new markerless registration method in cranio-maxillofacial surgery that is based on a high-resolution laser-scan of the patients (relaxed) skin surface. PATIENTS 20 patients with tumours, bone malformations or foreign bodies, scheduled for computer-assisted surgery, were involved in the study. STUDY DESIGN The clinically applied accuracy of the laser-scan-based registration was measured through additionally placed registration markers. The inherent precision of the laser-scan registration system was controlled in phantom studies. RESULTS The clinically applied accuracy of the new laser-scan-based registration technique ranged between 0.2 and 1.8 mm with a mean deviation of 1.1mm and a standard deviation of 0.3 mm. CONCLUSION The facial skin surface can serve as a sufficiently stable and invariable reference base in order to register patients for computer-assisted cranio-maxillofacial surgery.

Computer-assisted bone segment navigation

Computer-assisted bone segment navigation is defined as the precise 3-D positioning of geometrically mapped and mathematically described skeletal segments. These bone segments are osteotomized, fractured or prefabricated according to a surgical plan. The high-precision positioning should have an accuracy of 1 mm or better. Segment navigation should be prepared with plain computed tomography (CT) without the implantation of registration markers before CT in order to reduce the number of CTs and operations. The Surgical Segment Navigator (SSN) was developed at the University of Regensburg with the support of Carl Zeiss. This is the first system to meet these criteria. The SSN is based on an infrared positioning device which is connected to a Hewlett Packard LD Pro Workstation. Infrared transmitters are connected to individual templates which are fixed to the bone segment by osteosynthesis screws. Intraoperative correlation between surgical planning and surgical site is achieved by use of a surface-pattern of the bone segment which fits equally well to the laboratory model and the conditions encountered in the patient. The concept of the SSN was submitted by Carl Zeiss as German Patent DE 19747427 A1 in 1997. The SSN system presented here has already been applied clinically and its precision has been evaluated by bone segment navigation in human cadavers.

Surgical planning of computer-assisted repositioning osteotomies.

Repositioning osteotomies are frequently used in orthopedic surgery and traumatology to correct malpositions. Computed tomography (CT), stereolithographic models, and x-rays are used in planning. However, the precision achieved in the planning phase is usually not translated to patients. The Surgical Segment Navigator (SSN) is a navigation system that allows computer-assisted correction of malpositions. It consists of an infrared positioning device, two dynamic reference frames (DRF), an infrared pointer, and an infrared camera. All data are displayed numerically and graphically on the monitor of the SSN workstation. The Laboratory Unit for Computer-Assisted Surgery (LUCAS) is used for planning surgery in the laboratory. LUCAS requires only a native CT scan. A preparatory operation to implant bone markers that will be visible in x-rays and a further planning CT scan showing the bone markers, which were necessary with previous systems, are not required for the LUCAS and SSN system. This significantly reduces the radiation exposure of the patient and the costs of surgical planning. Measuring anatomical landmarks in the surgical site, which is time-consuming and reduces accuracy, is not required with the SSN system because the position of the infrared transmitters is known during surgical planning on the LUCAS workstation. This makes the surgical approach faster and much more precise. The surgical planning data are transferred to the surgical site using a data file and an individual surface pattern that fits the surface of the navigated bone segment. The data file is exported from the LUCAS-workstation to the SSN workstation. The planned spatial displacement of the infrared transmitters is saved in this file. The individual surface pattern carries the infrared transmitters. This pattern is the mechanical interface between infrared transmitters and navigated bone segment. The individual surface pattern can be polymerized directly on a small stereolithographic model of the navigated bone segment. The surface pattern can also be generated as negative form from a CT data set using a computer-assisted design/manufacture system. In summary, LUCAS and SSN allow for the computer-assisted correction of malpositions and positioning of artificial joints and implants. In principle, the systems can be used in all fields of surgery.

Soft tissue scanning for patient registration in image-guided surgery.

Prior to an image-guided surgical intervention, a correlation between the patients data set and the surgical site is required. This study introduces a markerless registration method for craniomaxillofacial surgery that is based on a high-resolution laser scan of the patients skin surface. The Surgical Segment Navigator SSN++ rejects contaminated surface measurements in a way similar to the bluescreen technique. Acquisition of the spatial position and the corresponding surface color of each laser-scanned point facilitates this bluescreen method, removing points with a defined surface color, e.g., blue or green points. The accuracy of the laser-scan-based registration was measured via additional intraoral titanium-markers. These markers served only to check the accuracy of the markerless registration process. In twelve patients, the stability and accuracy of the data set alignment was evaluated for high-(300,000 surface points), medium-, and low-resolution (down to 3,750 surface points) laser scanning. The accuracy of the registration technique was best for high-resolution laser scanning (mean deviation 1.1 mm; maximum deviation 1.8 mm). Low-resolution laser scans revealed inaccuracies up to 6 mm.

Development and First Patient Trial of a Surgical Robot for Complex Trajectory Milling

Objective: Todays surgical robots normally perform “simple” trajectories, e.g., assisting as tool-holding devices in neurosurgery, or milling linear paths for cavities in total hip replacement. From a clinical point of view, it is still a complex undertaking to implement robots in the operating room. Until now, robot systems have not been used in patient trials to mill “complex” trajectories, which involve many positional and orientation changes and are often necessary in cranio-maxillofacial (CMF) surgery. This paper presents the RobaCKa surgical robot system, which allows more precise execution of surgical interventions and milling of “complex” trajectories. Materials and Methods: The main components of the RobaCKa system are a (former) CASPAR robot system, a POLARIS system, and a force-torque sensor. Results: In the first patient trial (April 2003) the planned trajectory was executed with an error of 0.66 ± 0.2 mm. Conclusions: The use of former industrial robots for surgical applications is possible but complex. The advantages are improved precision and quality and the possibility of documentation. The use of such systems is normally limited to research institutions or large clinics, because it is hardly possible to implement the necessary technical and logistic efforts in routine surgical work.

System Design of a Hand-Held Mobile Robot for Craniotomy

This contribution reports the development and initial testing of a Mobile Robot System for Surgical Craniotomy, the Craniostar. A kinematic system based on a unicycle robot is analysed to provide local positioning through two spiked wheels gripping directly onto a patients skull. A control system based on a shared control system between both the Surgeon and Robot is employed in a hand-held design that is tested initially on plastic phantom and swine skulls. Results indicate that the system has substantially lower risk than present robotically assisted craniotomies, and despite being a hand-held mobile robot, the Craniostar is still capable of sub-millimetre accuracy in tracking along a trajectory and thus achieving an accurate transfer of pre-surgical plan to the operating room procedure, without the large impact of current medical robots based on modified industrial robots.

Computer-based approaches for maxillofacial interventions

Computers used as supporting tools for diagnostics, operation planning and therapy are of increasing relevance in surgery. Rapid progress in imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRT) and ultrasound already allows to represent anatomical and physiological conditions with maximal authenticity. In order to simulate complex surgeries we must develop ergonomic and intuitively useable software tools, thus enabling a precise and fast virtual execution of the planned surgical intervention preoperatively. Intraoperative support will consist of passive navigation tools, available already today, supporting the intraoperative orientation and, in the future, robots performing specific steps autonomously. Methods of augmented reality for the interaction of virtual objects and the real surgical scene are also suitable for the visualization of planning data and other medically relevant information in the operation situs. In maxillofacial and craniofacial surgery the techniques mentioned have been applied in all fields from dental implantology up to the correction of craniofacial malformations and the resection of skull base tumors. Many applications are still being developed or are still in the form of a prototype. However, it is already clear that developments in this area will have a considerable effect on a surgeons routine work.

Risk analysis for a reliable and safe surgical robot system

Abstract This paper shows the basic methods of quality assurance and risk analysis. It is particularly intended for researchers in the clinical field who have to implement safe systems with minimal resources and staff. The methods were applied to the robot system RobaCKa for craniotomies. Since surgical robots are complex mechatronic systems, it is important to apply systematic approaches for fault-free design, error detection and quality assurance. In universities and research centers, it is not often possible to apply the same measures for software design and error analysis as what is usually required for suppliers of medical products. Nevertheless, it is necessary to maintain basic regulations particularly for in vivo studies and clinical investigations. It makes sense to apply quality management right in the very beginning of a project, which would facilitate the its possible transformation into a commercial product later.

Next generation's navigation systems

Abstract The data set correlation between the surgical site and the corresponding image data set in the operating room (OR) is the most time-consuming process for the surgeon. The next generations navigation systems ought to perform an automatic and highly accurate patient registration in the OR. Two systems that meet these criteria, the Surgical Segment Navigator SSN and the SSN++, are described. The SSN was developed in 1997 and is used for computer-assisted bone segment navigation. The SSN++, which has further been developed since 1999, utilizes laser scans of the surgical site. Both systems perform patient registration by just one click on a command button.

Error Analysis of a Sub-millimeter Real-Time Target Recognition System with a Moving Camera

This paper discloses a method for simple and efficient optical coupling of a robotic arm with a tool with unknown location without exerting forces to the tool. Current solutions involve moving the robot in force-control mode and coupling by means of a manual gripper. This poses the problem with the transfer of unwanted forces to the tool while attempting to secure the design. With the intrinsic solution presented here, the camera is placed on the coupling axis and thence measures the distance and orientation to the target, the user will have the ability to safely guide the robotic arm towards the tool and smoothly couple the tool with the robots end effector. The mechanical prototype is not here described; this paper emphasizes the image processing, consequent data interpretation and general approach. After the explanation of the technique, its theoretical performance limit was examined and confirmed against the practically achieved performance.