Towards inferring positioning of straight cochlear-implant electrode arrays during insertion using real-time impedance sensing.

BACKGROUND: Cochlear-implant electrode arrays (EAs) are currently inserted with limited feedback, and impedance sensing has recently shown promise for EA localisation. METHODS: We investigate the use of impedance sensing to infer the progression of an EA during insertion. RESULTS: We show that the access resistance component of bipolar impedance sensing can detect when a straight EA reaches key anatomical locations in a plastic cochlea and when each electrode contact enters/exits the cochlea. We also demonstrate that dual-sided electrode contacts can provide useful proximity information and show the real-time relationship between impedance and wall proximity in a cadaveric cochlea for the first time. CONCLUSION: The access resistance component of bipolar impedance sensing has high potential for estimating positioning information of EAs relative to anatomy during insertion. Main limitations of this work include using saline as a surrogate for human perilymph in ex vivo models and using only one type of EA.

Robotic pullback technique of a precurved cochlear-implant electrode array using real-time impedance sensing feedback.

PURPOSE: During traditional insertion of cochlear implant (CI) electrode arrays (EAs), surgeons rely on limited tactile feedback and visualization of the EA entering the cochlea to control the insertion. One insertion approach for precurved EAs involves slightly overinserting the EA and then retracting it slightly to achieve closer hugging of the modiolus. In this work, we investigate whether electrical impedance sensing could be a valuable real-time feedback tool to advise this pullback technique. METHODS: Using a to-scale 3D-printed scala tympani model, a robotic insertion tool, and a custom impedance sensing system, we performed experiments to assess the bipolar insertion impedance profiles for a cochlear CI532/632 precurved EA. Four pairs of contacts from the 22 electrode contacts were chosen based on preliminary testing and monitored in real time to halt the robotic insertion once the closest modiolar position had been achieved but prior to when the angular insertion depth (AID) would be reduced. RESULTS: In this setting, the open-loop robotic insertion impedance profiles were very consistent between trials. The exit of each contact from the external stylet of this EA was clearly discernible on the impedance profile. In closed-loop experiments using the pullback technique, the average distance from the electrode contacts to the modiolus was reduced without greatly affecting the AID by using impedance feedback in real time to determine when to stop EA retraction. CONCLUSION: Impedance sensing, and specifically the access resistance component of impedance, could be a valuable real-time feedback tool in the operating room during CI EA insertion. Future work should more thoroughly analyze the effects of more realistic operating room conditions and inter-patient variability on this technique.

Patient-specific, touch-based registration during robotic, image-guided partial nephrectomy.

Image-guidance during partial nephrectomy enables navigation within the operative field alongside a 3-dimensional roadmap of renal anatomy generated from patient-specific imaging. Once a process is performed by the human mind, the technology will allow standardization of the task for the benefit of all patients undergoing robot-assisted partial nephrectomy. Any surgeon will be able to visualize the kidney and key subsurface landmarks in real-time within a 3-dimensional simulation, with the goals of improving operative efficiency, decreasing surgical complications, and improving oncologic outcomes. For similar purposes, image-guidance has already been adopted as a standard of care in other surgical fields; we are now at the brink of this in urology. This review summarizes touch-based approaches to image-guidance during partial nephrectomy, as the technology begins to enter in vivo human evaluation. The processes of segmentation, localization, registration, and re-registration are all described with seamless integration into the da Vinci surgical system; this will facilitate clinical adoption sooner.

Design Considerations for a Steerable Needle Robot to Maximize Reachable Lung Volume.

Steerable needles that are able to follow curvilinear trajectories and steer around anatomical obstacles are a promising solution for many interventional procedures. In the lung, these needles can be deployed from the tip of a conventional bronchoscope to reach lung lesions for diagnosis. The reach of such a device depends on several design parameters including the bronchoscope diameter, the angle of the piercing device relative to the medial axis of the airway, and the needle's minimum radius of curvature while steering. Assessing the effect of these parameters on the overall system's clinical utility is important in informing future design choices and understanding the capabilities and limitations of the system. In this paper, we analyze the effect of various settings for these three robot parameters on the percentage of the lung that the robot can reach. We combine Monte Carlo random sampling of piercing configurations with a Rapidly-exploring Random Trees based steerable needle motion planner in simulated human lung environments to asymptotically accurately estimate the volume of sites in the lung reachable by the robot. We highlight the importance of each parameter on the overall system's reachable workspace in an effort to motivate future device innovation and highlight design trade-offs.

Accuracy of Touch-Based Registration During Robotic Image-Guided Partial Nephrectomy Before and After Tumor Resection in Validated Phantoms.

Aim: Image-guided surgery (IGS) allows for accurate, real-time localization of subsurface critical structures during surgery. No prior IGS systems have described a feasible method of intraoperative reregistration after manipulation of the kidney during robotic partial nephrectomy (PN). We present a method for seamless reregistration during IGS and evaluate accuracy before and after tumor resection in two validated kidney phantoms. Materials and Methods: We performed robotic PN on two validated kidney phantoms-one with an endophytic tumor and one with an exophytic tumor-with our IGS system utilizing the da Vinci Xi robot. Intraoperatively, the kidney phantoms' surfaces were digitized with the da Vinci robotic manipulator via a touch-based method and registered to a three-dimensional segmented model created from cross-sectional CT imaging of the phantoms. Fiducial points were marked with a surgical marking pen and identified after the initial registration using the robotic manipulator. Segmented images were displayed via picture-in-picture in the surgeon console as tumor resection was performed. After resection, reregistration was performed by reidentifying the fiducial points. The accuracy of the initial registration and reregistration was compared. Results: The root mean square (RMS) averages of target registration error (TRE) were 2.53 and 4.88 mm for the endophytic and exophytic phantoms, respectively. IGS enabled resection along preplanned contours. Specifically, the RMS averages of the normal TRE over the entire resection surface were 0.75 and 2.15 mm for the endophytic and exophytic phantoms, respectively. Both tumors were resected with grossly negative margins. Point-based reregistration enabled instantaneous reregistration with minimal impact on RMS TRE compared with the initial registration (from 1.34 to 1.70 mm preresection and from 1.60 to 2.10 mm postresection). Conclusions: We present a novel and accurate registration and reregistration framework for use during IGS for PN with the da Vinci Xi surgical system. The technology is easily integrated into the surgical workflow and does not require additional hardware.

Comparing the accuracy of the da Vinci Xi and da Vinci Si for image guidance and automation.

BACKGROUND: Current laparoscopic surgical robots are teleoperated, which requires high fidelity differential motions but does not require absolute accuracy. Emerging applications, including image guidance and automation, require absolute accuracy. The absolute accuracy of the da Vinci Xi robot has not yet been characterized or compared to the Si system, which is now being phased out. This study compares the accuracy of the two. METHODS: We measure robot tip positions and encoder values assessing accuracy with and without robot calibration. RESULTS: The Si is accurate if the setup joints are not moved but loses accuracy otherwise. The Xi is always accurate. CONCLUSION: The Xi can achieve submillimetric average error. Calibration improves accuracy, but excellent baseline accuracy of the Xi means that calibration may not be needed for some applications. Importantly, the external tracking systems needed to account for setup joint error in the Si are no longer required with the Xi.

Decoupling Steerability from Diameter: Helical Dovetail Laser Patterning for Steerable Needles.

The maximum curvature of a steerable needle in soft tissue is highly sensitive to needle shaft stiffness, which has motivated use of small diameter needles in the past. However, desired needle payloads constrain minimum shaft diameters, and shearing along the needle shaft can occur at small diameters and high curvatures. We provide a new way to adjust needle shaft stiffness (thereby enhancing maximum curvature, i.e. "steerability") at diameters selected based on needle payload requirements. We propose helical dovetail laser patterning to increase needle steerability without reducing shaft diameter. Experiments in phantoms and ex vivo animal muscle, brain, liver, and inflated lung tissues demonstrate high steerability in soft tissues. These experiments use needle diameters suitable for various clinical scenarios, and which have been previously limited by steering challenges without helical dovetail patterning. We show that steerable needle targeting remains accurate with established controllers and demonstrate interventional payload delivery (brachytherapy seeds and radiofrequency ablation) through the needle. Helical dovetail patterning decouples steerability from diameter in needle design. It enables diameter to be selected based on clinical requirements rather than being carefully tuned to tissue properties. These results pave the way for new sensors and interventional tools to be integrated into high-curvature steerable needles.

STEERABLE NEEDLE TRAJECTORY FOLLOWING IN THE LUNG: TORSIONAL DEADBAND COMPENSATION AND FULL POSE ESTIMATION WITH 5DOF FEEDBACK FOR NEEDLES PASSING THROUGH FLEXIBLE ENDOSCOPES.

Bronchoscopic diagnosis and intervention in the lung is a new frontier for steerable needles, where they have the potential to enable minimally invasive, accurate access to small nodules that cannot be reliably accessed today. However, the curved, flexible bronchoscope requires a much longer needle than prior work has considered, with complex interactions between the needle and bronchoscope channel, introducing new challenges in steerable needle control. In particular, friction between the working channel and needle causes torsional windup along the bronchoscope, the effects of which cannot be directly measured at the tip of thin needles embedded with 5 degree-of-freedom magnetic tracking coils. To compensate for these effects, we propose a new torsional deadband-aware Extended Kalman Filter to estimate the full needle tip pose including the axial angle, which defines its steering direction. We use the Kalman Filter estimates with an established sliding mode controller to steer along desired trajectories in lung tissue. We demonstrate that this simple torsional deadband model is sufficient to account for the complex interactions between the needle and endoscope channel for control purposes. We measure mean final targeting error of 1.36 mm in phantom tissue and 1.84 mm in ex-vivo porcine lung, with mean trajectory following error of 1.28 mm and 1.10 mm, respectively.

Computer-assisted planning for a concentric tube robotic system in neurosurgery.

PURPOSE: Laser-induced thermotherapy in the brain is a minimally invasive procedure to denature tumor tissue. However, irregularly shaped brain tumors cannot be treated using existing commercial systems. Thus, we present a new concept for laser-induced thermotherapy using a concentric tube robotic system. The planning procedure is complex and consists of the optimal distribution of thermal laser ablations within a volume as well as design and configuration parameter optimization of the concentric tube robot. METHODS: We propose a novel computer-assisted planning procedure that decomposes the problem into task- and robot-specific planning and uses a multi-objective particle swarm optimization algorithm with variable length. RESULTS: The algorithm determines a Pareto-front of optimal ablation distributions for three patient datasets. It considers multiple objectives and determines optimal robot parameters for multiple trajectories to access the tumor volume. CONCLUSIONS: We prove the effectiveness of our planning procedure to enable the treatment of irregularly shaped brain tumors. Multiple trajectories further increase the applicability of the procedure.