Haptic-Assisted Collaborative Robot Framework for Improved Situational Awareness in Skull Base Surgery

TL;DR

The paper tackles the challenge of safe skull base drilling near critical structures by introducing a haptic-assisted cooperative robot framework that leverages patient-specific distance fields to generate virtual fixtures. It integrates CT-derived SDFs with an AMBF-based dynamic simulation and an admittance-controlled REMS robot to provide progressive, directionally guided haptic feedback during drilling. Key contributions include a complete pipeline from anatomy constraint creation to intraoperative haptic assistance and initial feasibility demonstrations in phantom and cadaver models, showing improved safety for both novices and experienced surgeons. The approach preserves surgeon autonomy while enhancing situational awareness and has potential for broad applicability due to its open-source, modular integration with standard imaging and robotic platforms.

Abstract

Skull base surgery is a demanding field in which surgeons operate in and around the skull while avoiding critical anatomical structures including nerves and vasculature. While image-guided surgical navigation is the prevailing standard, limitation still exists requiring personalized planning and recognizing the irreplaceable role of a skilled surgeon. This paper presents a collaboratively controlled robotic system tailored for assisted drilling in skull base surgery. Our central hypothesis posits that this collaborative system, enriched with haptic assistive modes to enforce virtual fixtures, holds the potential to significantly enhance surgical safety, streamline efficiency, and alleviate the physical demands on the surgeon. The paper describes the intricate system development work required to enable these virtual fixtures through haptic assistive modes. To validate our system's performance and effectiveness, we conducted initial feasibility experiments involving a medical student and two experienced surgeons. The experiment focused on drilling around critical structures following cortical mastoidectomy, utilizing dental stone phantom and cadaveric models. Our experimental results demonstrate that our proposed haptic feedback mechanism enhances the safety of drilling around critical structures compared to systems lacking haptic assistance. With the aid of our system, surgeons were able to safely skeletonize the critical structures without breaching any critical structure even under obstructed view of the surgical site.

Figures (8)

• Figure 1: Conceptual overview of the safety-driven haptic assistance: Compliance force, defining the preferred direction of the drill based on the robot’s position relative to the anatomical surface, is computed in the dynamic simulation model. This force is fed back to the collaborative robot controller to generate haptic feedback.
• Figure 2: System Overview. (Left) preoperative creation of patient model and constraints configuration. (Middle) intra-operative dynamic simulation of the robot and anatomy as well as generation of compliance force. (Right) Collaborative robot receiving the compliance force and augment the safety of the procedure.
• Figure 3: Here, $F_H$ denotes the user-applied force and $F_C$ is the compliance force generated using SDF. The compliance force is proportional to the user-applied force and effectively guides the motion in the preferred direction.
• Figure 4: Frame transform diagram. $F_{OT}$ represents the frame for the optical tracker. $F_{drill}$, $F_{ref}$ are the frames attached to the drill marker and reference marker, respectively. $F_{EE}$ is the frame for the robot end-effector.
• Figure 5: Experimental setup. Surgeon uses the surgical drill attached to the robot under microscopic view. Optical tracker was located next to the robot to monitor both the drill and the anatomy.