User Performance of VR-Based Dissection: Direct Mapping and Motion Coupling of a Surgical Tool

Robot-assisted surgical systems aim at enhancing surgeon's skills. Nonetheless, the learning curve for mastering such systems is very slow due to the motion-coupling mode that is usually presented in these systems for manipulating a surgical tool. This mode has limitations compared to the direct mapping mode used in open surgery for manipulating a tool. Virtual reality (VR) surgical simulators may reduce the learning time for transferring the surgeon's skills from direct mapping to motion coupling of tool manipulation. This may be accomplished by adding two features to the simulator. First, force models of tool-tissue interaction can be implemented in the haptic interface of the simulator. Second, VR-based surgical tasks can be designed to recreate direct-mapping mode and motion coupling mode of tool manipulation, as in open and robot-assisted surgeries, respectively. This may permit to transfer the surgeon's skills from open surgery to robot-assisted surgery in a timely manner. This work presents a preliminary study on the effect of direct mapping mode and motion coupling mode of tool manipulation on the performance of naive participants for VR-based brain tissue dissection. An Analytic force model of soft-tissue dissection was implemented in the simulator along with visual feedback of a predefined tool speed of 1 mm/s, which is observed in neurosurgery. The outcomes indicated that the motion quality of the tool via direct mapping was significantly better than with motion coupling. Thus, the study might serve as a first step toward the assessment of user's skills for VR-based robot-assisted dissection.