SlicerIGT is a collection of modules for the 3D Slicer application to support image-guided medical interventions
Surgical device & robot registration
Surgical robotics has received significant publicity lately. Recent demonstrations of trans-Atlantic telesurgical gallbladder removal with the Zeus system or coronary bypass surgery on a beating heart with the daVinci system received much attention. Several robotic systems are available for commercial use in the US, yet many of their fundamental operating principles are still unknown or not studied in a through and systematic manner. One of such unexplored issues is robustness of the registration between the robot’s coordinate frame and the reference system in which the anatomical target is located. Clearly, this is one of the most critical elements in computational image-guided surgical robotic system. Failure of registration will either cause harm to the patient if remains unnoticed or it causes the surgical procedure to halt, which may cerate equally dangerous situations. Typically, we track the robot’s end-effector by the means of some imaging device. Ideally, this imaging device is the same anatomical or physiological medical imaging modality (X-ray, MRI, Ultrasound) that guides the surgical procedure, but it can be some infrared or visible light snapshot or video stream. In order to facilitate registration, we attach precision machined localizer fixtures (a.k.a fiducials) to the end-effector [1,2,3,4], as shown in Figure 1. |
Loss of stereotactic registration information is inevitable in surgical robotic systems, yet this issue has not been researched in depth. Our group has published results of an initial investigation that revealed intriguing results and many implications to manufacturing of registration hardware and software. We also concluded that new research should be directed toward analyzing the performance of currently known systems and development of novel computational solutions and registration methods that perform robustly under sub-optimal working conditions, when only partial information is available. As robotic technology advances, end-effectors can be introduced into the body, in order to control surgical tools locally. When the end-effector is inside the body, the localizer fixture must be built inside the end-effector, in a way that it does not obstruct the actuation mechanism. This restriction calls for geometrically complex fiducial patterns, which, in turn, also require computationally intensive numerical solutions. On the other hand, novel high-performance numerical computational methods could also make sophisticated fiducial patterns technically feasible.