Image-Based Closed-Loop Control of a Robotically Steerable Endoscopic Cannula for Minimally Invasive Neurosurgery

Abstract

Robot-assisted minimally invasive neurosurgeries have shown great promise in enabling lower invasiveness and faster patient recovery times. However, performing such surgeries remains challenging, mainly due to the use of rigid surgical tools and limited accessibility to deep-seated brain structures. Employing robotically steerable tools could address these challenges, as these devices, being relatively more dexterous, can gain access to different regions in the brain. The autonomous control of these tools could further enable manipulation with higher precision and lower procedural time, facilitating less fatigue for surgeons. In this paper, we present a control strategy for the precise manipulation of a robotically steerable endoscopic cannula (RSEC). The proposed control architecture uses a combination of inverse kinematics, endoscopic imaging, and electromagnetic tracking feedback to perform task-space control of the RSEC in real-time. A joint angle estimation algorithm is proposed to estimate the bending angles of the RSEC using an endoscopic camera. The tip-position RMSE value of the RSEC when bending the proximal and distal joints, obtained using the proposed control strategy, was 0.7 mm. The results indicate that the proposed method can be used to achieve position control of the RSEC with sub-mm accuracy.