IROS2019国际学术会议论文集0495

Abstract Vascular diseases are the most common precursors to ischemic heart disease and stroke, which are two of the leading causes of death worldwide. Endovascular intervention is a minimally invasive surgical approach to treat such diseases. Compared to open surgery, it has the advantages of faster recovery, reduced need for general anesthesia, reduced blood loss and significantly lower mortality. Endovascular procedures require high surgical skills to minimize contacts between the manipulated instruments (catheters and guidewires) and the vessel wall, which represent one of the major risks for the patient. Robotic assistance can potentially improve the precision and stability of instruments manipulation. One key limitation of current commercial robotic platforms is the lack of haptic feedback, preventing their acceptance and limiting the clinical usability. This paper proposes to bring the benefit of haptic feedback to robot- assisted endovascular intervention. Here we hypothesize that the introduction of 3D haptic guidance during robot-assisted endovascular procedure can further improve the surgical performance and safety while overcoming the limitations of currently available technology. The proposed 3D haptic guidance allows the surgeon to sense the vasculature while controlling a catheter through a robotic haptic manipulator. Validation of the system is performed through end-user experiments with vascular surgeons on a bespoke surgical simulator. The obtained results demonstrate that 3D haptic guidance has the potential of improving effectiveness, precision, and safety of endovascular intervention. Furthermore, vascular surgeons found the proposed technology safe and overall easy to use, indicating its potential on real surgical procedures. I. INTRODUCTION Thanks to the technological progression since the 1980s, Minimally Invasive Surgery (MIS) has become an established approach across many surgical specialties 1. Despite its advantages over traditional open surgery (quicker patient recovery, reduced tissue disruption and hospitalization costs), MIS procedures can be ergonomically difficult to perform due to the use of rigid instruments, limited sensory feedback, misalignment of visuo-motor axes, and the need for high dexterity. In response to these limitations, robotics and computer assistance have been integrated into the clinical workflow to provide augmentation of surgical skills in terms of enhanced dexterity and precision. However, such systems effectively decouple surgeons actions from their resulting interactions with the surgical site, thus depriving surgeons of tactile feedback. M. B. Molinero, G. Dagnino, J. Liu, W. Chi, M. E. M. K Abdelaziz, and G.Z. Yang are with The Hamlyn Centre for Robotic Surgery, Imperial College London, UK. (e-mail: dagnino.giulio). T.M.Y. Kwok and C. Riga are with the Department of Surgery Amigo (Catheter Robotics Inc. NJ, USA), CorPath GRX (Corindus Vascular Robotics, MA, USA) and the R-one (Robocath, France) which manipulate standard catheters and guidewires. Unfortunately, none of the above platforms (excluding Sensei X2) provides haptic feedback. Therefore, contact forces M. B. Molinero, G. Dagnino, J. Liu, W. Chi, M. E. M. K. Abdelaziz, T.M.Y. Kwok, C. Riga, and G.Z. Yang Haptic Guidance for Robot-Assisted Endovascular Procedures: Implementation and Evaluation on Surgical Simulator Figure 1. The CathBot system. Motion command on the master manipulator (A) are sent to the slave robot (B) to manipulate a catheter or a guidewire while the 2D navigation system (not used in the work reported in this paper) provides visual guidance (C). Adapted from 17. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Macau, China, November 4-8, 2019 978-1-7281-4003-2/19/$31.00 2019 IEEE5398 between the manipulated instruments and the anatomy are not perceived by the surgeon, thus making the already challenging tasks even more complex and dangerous for the patient. Solutions to this problem have been proposed by several research groups 614 by embedding force sensors at the proximal or distal portion of the catheter to provide haptic feedback. However, incorporating force sensors into small instruments like catheters and guidewires is practically difficult, expensive, and time-consuming; also, it requires modifying commercial medical instruments, thus introducing clinical usability issues such as safety, biocompatibility, and sterilization. Vision-based force sensing through shape and position analysis of the catheter and the vasculature is a promising solution as demonstrated in preliminary work reported in 1517. Here, catheter and vasculature are segmented on intra-operative fluoroscopic images and used to provide real-time haptic feedback to the surgeon based on catheter/vessel relative proximity. However, the 2D nature of fluoroscopic imaging used in these works limits the haptic feedback to the imaging plane, i.e. the catheter/vessel contacts out of the imaging plane - or normal to the imaging plane - are not detected. This clearly represents a major limitation in terms of clinical usability. In this paper we hypothesize that the introduction haptic feedback based on 3D vision (hereafter called 3D vision- based haptic guidance) during robot-assisted endovascular procedures can further improve surgical performance and safety while overcoming the issues highlighted above. We describe the design, development, and assessment of 3D dynamic haptic guidance for our robotic platform, the CathBot. The main objective of this research is the design and evaluation of the 3D haptic guidance, with the resulting contributions: 1) presenting the first framework for the creation and testing of haptic feedback and active constraints in endovascular procedures based on 3D vision; 2) pilot end- user study with vascular surgeons to understand its applicability, usefulness, and acceptance to robot-assisted endovascular procedures. Section II introduces the Hamlyns CathBot system, and describes the 3D vision-based haptic guidance and the methodology to create active constraints; Section III presents the surgical simulator developed to implement and test the 3D vision-based haptic guidance under controlled and repeatable conditions, and its integration with the CathBot system; Section IV reports the experimental evaluation including setup, results, and discussion; Section V concludes the paper, offering future research directions. II. HAPTIC GUIDANCE BASED ON 3D VISION Research in our group at the Hamlyn Centre focuses on overcoming the current principal technological limitations of endovascular procedures (namely limited catheter/guidewire maneuverability, lack of adequate 3D navigation, and lack of contact force sensing 5) by creating the CathBot robotic platform 13, 17, 18. This is a teleoperated system in a master-slave configuration that provides improved catheter/guidewire maneuverability and control by offering an ergonomic manipulator that is designed to transmit haptic feedback to the operator (Fig.1). Force feedback is generated through vision by tracking the tip of the catheter and the vasculature on 2D images 17 (please refer to the online video contribution in 17). However, as anticipated before, with this technique 3D anatomy and depth information are lost, and the haptic feedback is calculated only on the imaging plane, representing a major limitation of the system in terms of clinical usability and safety. This paper continues the improvement in haptic feedback by introducing for the first time haptic guidance based on 3D vision to robot- assisted endovascular procedures. This represents also a step forward towards introducing dynamic navigation into endovascular procedures one of the CathBot future objectives. Figure 2 shows how haptic guidance based on 3D vision is created. The haptic device used in this work is the CathBot manipulator introduced in 17 (Fig.1A and 2A). The main component of the manipulator is a tube-like structure that can be moved linearly and rotated axially. Any linear movement causes a railing to slide through a Linear Motor (LM) (LM1247, Faulhaber), while the torque Figure 2. 3D vision-based haptic guidance: the surgeon manipulates the catheter through the CathBot mechanical manipulator (A). The 3D navigation system (a surgical simulator in this research) calculates the 3D distance and orientation of the catheter tip with respect to the vessel wall (B,C). This information is processed by the controller and coupled with surgeons motion commands to generate force feedback and haptic guidance. 5399 generated by rotations is transmitted through a belt to a Brushless Motor (BLM) (BLDC 2057B, Faulhaber). LM and BLM are used to generate motion commands on the slave side of the system (i.e. to move a catheter or guidewire) and to transmit haptic feedback back to the user. Haptic feedback is perceived as frictions which increase proportionally to the catheter-vessel distance (i.e. the closer the catheter is to the vessel, the higher is the force feedback generated into the master manipulator to inform the surgeon of the proximity of the wall). Contacts between catheter and vessel wall should not be completely avoided as they are used by the operator to navigate the instrument (usually not steerable) through the vasculature. However, the magnitude of the catheter-vessel hits should be minimized while allowing operator to freely navigate the instrument. Therefore, friction-like forces were chosen for haptic rendering, instead of other options (e.g. repulsive forces). Here we propose to solve the problem by measuring the distance from the instrument tip (e.g. a catheter) and a 3D model of the vasculature generated pre- operatively by CT data. This is done by acquiring patient- specific CT images and segmenting them using dedicated software (in our case 3D Slicer). The 3D pose (position and orientation) of the catheter tip Pt = xt, yt, zt, xt, yt, zt is provided by the simulator described in the next section. In a real case, Pt can be provided by the NDI Aurora system (as in 18) by placing an EM 6DOF sensor to the tip of the catheter. To calculate the 3D distance between the instrument tip and the vessel wall a bespoke tracking method was developed, including ray casting and collision detection algorithms. Firstly (Fig.2B), a ray casting algorithm is applied: 100 equally-spaced rays are cast in different directions starting at the actual position of the catheter tip (these rays form a sphere, i.e. the red sphere in Fig.2B). Secondly, the collision detection algorithm detects the collisions between the rays and the colliders placed on the vessel wall. Finally (Fig.2C), the algorithm selects the point on the vessel wall Pw = xw, yw, zw, xw, yw, zw with minimum distance d from the catheter tip Pt. This information is then used to model the damping factor f as described in (1): = (1) = |2 1|( 1) + 1 (2) where d is the Cartesian distance between the catheter tip Pt and the vessel wall Pw; D is the local vessel diameter; fmax is the maximum friction achievable. z is the angle between the longitudinal axes of the catheter tip Pt and the normal to the vessel wall in Pw. z is computed using the quaternion product between the QPw quaternion (orientation of the vessel wall) and the inverse of the QPt quaternion (pose of the catheter tip): = () 1 = 0 0 0 0 0 0 (3) = acos(), 1 = 1 (4) where, wi,ax,by,cz are components of the quaternions. It is wort noting that if the catheter tip is in contact with (d = 0) or perpendicular to (z = 0) the vessel wall, then f is equal to fmax. The damping modelled in (1) is then used in (5) to provide haptic feedback perceived as a friction to the surgeon through the haptic manipulator (Fig.2). The LM and BLM generates respectively the linear friction (perceived when pushing and pulling the catheter) and the rotational friction (perceived when twisting the catheter). The haptic feedback is generated for both LB and BLM as 19: = (5) where Vm is the motors velocity (control output), I is the motor current (control input), and f is the damping derived in (1). When the surgeon applies a force on the tube handle to manipulate the catheter, a motor current I proportional to the force applied is generated. The corresponding motor velocity (LM and BLM) is directly proportional to the force applied (described by I) and inversely to the damping factor f. Considering (1) and (5), this means that when the surgeon pushes the catheter towards the vessel wall, then the friction generated by the motors (equation (5) increases accordingly. We modelled this friction to act as repulsive active constraint that guide the surgeon in manipulating the catheter through the vasculature. It is also dynamic, as it adapts to the motion of the vasculature (due to heartbeat and respiration), thus further minimizing undesired and dangerous contacts between the manipulated instrument and the vessel. Here we hypothesize that such haptic guidance can provide enhanced assistance to the surgeon and improve the performance and safety of this delicate procedure. However, implementing the proposed haptic guidance in a real endovascular procedure is still an open challenge. The main issue currently is the lack of dynamic real-time intra-operative 3D imaging (current practice generally relies on 2D fluoroscopy or statically registered pre-operatively generated 3D models). Therefore, in order to assess the proposed 3D vision-based haptic guidance, we have developed the surgical simulator for robot-assisted endovascular procedures which is described in the next section. III. SURGICAL SIMULATOR FOR ROBOT-ASSISTED ENDOVASCULAR PROCEDURES A surgical simulator was developed in Unity to assess the proposed 3D vision-based haptic guidance under controllable and repeatable experimental conditions (Fig.3). The simulator features: 1) generation and visualization of a 3D dynamic model of the vasculature based on pre-operative CT images; 2) modeling of a vascular catheter based on pre-operative CT data; 3) implementation of 3D haptic guidance as described 5400 above; and 4) assessment of users performance through evaluation metrics. In this study a model of an aortic arch (Fig.3A,B,C) was generated from pre-operative CT images generated by 4D XCAT 20. The model was rigged and animated in Blender to simulate the motions due to heartbeat and breathing. The aortic arch model was animated to replicate realistic movements for a more realistic representation of a real intervention. This was achieved by incorporating in our model the results obtained by Beller et al. 21 and Weber et al. 22 on the aortic arch dynamic due to heartbeat and respiratory cycle respectively. The final animated model was then imported into Unity where physics was applied to allow interaction with other elements (i.e. the catheter model) in the simulated environment. A similar procedure was followed to model the catheter (Fig.3D,E,F). A commercial 5-Fr angiography catheter (Beacon Tip Van Schie 1 by Cook Medical, USA) was CT scanned and segmented to generate a 3D model. The model was rigged in Blender to create a skeleton that determines where the model can bend or deform. Once imported into Unity, a mesh collider was assigned to each part forming the model and then linked together through configurable joints to model the physics of the catheter. This was necessary to ensure that the catheter model behaved realistically and interacted properly with the aortic arch model (Fig.3G). Once both the aortic arch and catheter models were imported into the simulation environment, the colliders assigned, and the physics correctly modeled, the 3D haptic guidance was implemented as described in section II. As a result, the user could navigate the model of the catheter within the 3D virtual vasculature while receiving haptic guidance through the master manipulator connected to the simulator. The pose of the catheter tip Pt is provided by the simulator in real-time. Lastly, the simulator was designed to assess users performance in terms of catheter navigation precision under two different conditions: C.1) no constraints, and C.2) 3D vision-based haptic guidance. For each condition, two different tasks were available: T.1) cannulation of the left ventricle (LV), and T.2) cannulation of the left subclavian artery (LSA); both via retrograde infra-inguinal access. Several evaluation metrics were designed and embedded into the simulator to objectively assess the users performance. Namely: 1) distance between the catheter tip and the vessel wall; 2) number of tip/vessel contacts; 3) distance between the catheter tip and center of the vessel; 4) time to complete the task. These metrics - recorded during the surgical simulations - were then used off-line to calculate two more metrics: 5) average speed of the catheter and 6) path following RMSE (tip path vs vessel center line). IV. EXPERIMENTAL EVALUATION This section reports the experimental setup and methodology used to evaluate whether the proposed 3D vision-based haptic guidance can improve the surgical performance. The readers are referred to the attached video for a visual overview of the experimental evaluation. The master manipulator and the surgical simulator were used in an experiment designed to compare as introduced earlier two different conditions, i.e. C.1) no constraints, and C.2) 3D vision-based haptic guidance. 3 endovascular-trained vascular surgeons were asked to complete two different cannulation procedures, i.e. T.1) cannulation of the LV, and T.2) cannulation of the LSA. The users watched the simulated scene, consisting of an animated aortic arch and a catheter model, on