In robot-assisted partial nephrectomy, kidney tumors are removed surgically, along with a margin of healthy tissue around the tumor. This margin is taken to prevent positive margins (i.e. tumor tissue accidentally left behind). Yet it is desirable to minimize the thickness of the margin, since the effectiveness of post-surgery kidney function is related to the volume of kidney tissue preserved. In this paper we use statistical information on robot-mediated surface-based registration to select variable-thickness margins that optimally account for registration uncertainty. These margins can then be displayed in the surgeon console to provide enhanced information to the surgeon during robotic partial nephrectomy.

Partial nephrectomy involves removing a tumor while sparing surrounding healthy kidney tissue. Compared to total kidney removal, partial nephrectomy improves outcomes for patients but is underutilized because it is challenging to accomplish minimally invasively, requiring accurate spatial awareness of unseen subsurface anatomy. Image guidance can enhance spatial awareness by displaying a 3D model of anatomical relationships derived from medical imaging information. It has been qualitatively suggested that the da Vinci robot is well suited to facilitate image guidance through touch-based registration. In this paper we validate and advance this concept toward real-world use in several important ways. First, we contribute the first quantitative accuracy evaluation of touch-based registration with the da Vinci. Next, we demonstrate real-time, touch-based registration and display of medical images for the first time. Lastly, we perform the first experiments validating use of touch-based image guidance to improve a surgeon’s ability to localize subsurface anatomical features in a geometrically realistic phantom.

Our overarching goal is to facilitate wider adoption of robot-assisted partial nephrectomy through image- guidance, which can enable a surgeon to visualize subsurface features and instrument locations in real time intraoperatively. This is motivated by the observation that while there are compelling lifelong health benefits of partial nephrectomy, radical nephrectomy remains an overused surgical approach for many kidney cancers. Image-guidance may facilitate wider adoption of the procedure because it has the potential to increase surgeons' confidence in efficiently and safely exposing critical structures as well as achieving negative margins with maximal benign tissue sparing, particularly in a minimally invasive setting. To maintain the accuracy of image-guidance during the procedure as the kidney moves, periodic re-registration of medical image data to kidney anatomy is necessary. In this paper, we evaluate three registration approaches for the da Vinci Surgical System that have the potential to enable real-time updates to the display of segmented preoperative images within its console. Specifically, we compare the use of surface ink fiducials triangulated from stereo endoscope images, point clouds obtained without fiducials using a stereoscopic depth mapping algorithm, and points obtained by lightly tracing the da Vinci tool tip over the kidney surface. We compare and contrast the three approaches from both an accuracy and a workflow perspective.

This letter presents a novel miniature robotic endoscope that is small enough to pass through the Eustachian tube and provides visualization of the middle ear (ME). The device features a miniature bending tip previously conceived of as a small-scale robotic wrist that has been adapted to carry and aim a small chip-tip camera and fiber-optic light sources. The motivation for trans-Eustachian tube ME inspection is to provide a natural-orifice-based route to the ME that does not require cutting or lifting the eardrum, as is currently required. In this letter, we first perform an analysis of the ME anatomy and use a computational design optimization platform to derive the kinematic requirements for endoscopic inspection of the ME through the Eustachian tube. Based on these requirements, we fabricate the proposed device and use it to demonstrate the feasibility of ME inspection in an anthropomorphic model, i.e., a 3-D printed ME phantom generated from patient image data. We show that our prototype provides > 74% visibility coverage of the sinus tympani, a region of the ME crucial for diagnosis, compared to an average of only 6.9% using a straight nonarticulated endoscope through the Eustachian tube.

HYPOTHESIS An image-guided robotic system can safely perform the bulk removal of bone during the translabyrinthine approach to vestibular schwannoma (VS). BACKGROUND The translabyrinthine approach to VS removal involves extensive manual milling in the temporal bone to gain access to the internal auditory canal (IAC) for tumor resection. This bone removal is time consuming and challenging due to the presence of vital anatomy (e.g., facial nerve) embedded within the temporal bone. A robotic system can use preoperative imaging and segmentations to guide a surgical drill to remove a prescribed volume of bone, thereby preserving the surgeon for the more delicate work of opening the IAC and resecting the tumor. METHODS Fresh human cadaver heads were used in the experiments. For each trial, the desired bone resection volume was planned on a preoperative computed tomography (CT) image, the steps in the proposed clinical workflow were undertaken, and the robot was programmed to mill the specified volume. A postoperative CT scan was acquired for evaluation of the accuracy of the milled cavity and examination of vital anatomy. RESULTS In all experimental trials, the facial nerve and chorda tympani were preserved. The root mean squared surface accuracy of the milled cavities ranged from 0.23 to 0.65 mm and the milling time ranged from 32.7 to 57.0 minute. CONCLUSION This work shows feasibility of using a robot-assisted approach for VS removal surgery. Further testing and system improvements are necessary to enable clinical translation of this technology.

Safe and effective planning for robotic surgery that involves cutting or ablation of tissue must consider all potential sources of error when determining how close the tool may come to vital anatomy. A pre-operative plan that does not adequately consider potential deviations from ideal system behavior may lead to patient injury. Conversely, a plan that is overly conservative may result in ineffective or incomplete performance of the task. Thus, enforcing simple, uniform-thickness safety margins around vital anatomy is insufficient in the presence of spatially varying, anisotropic error. Prior work has used registration error to determine a variable-thickness safety margin around vital structures that must be approached during mastoidectomy but ultimately preserved. In this paper, these methods are extended to incorporate image distortion and physical robot errors, including kinematic errors and deflections of the robot. These additional sources of error are discussed and stochastic models for a bone-attached robot for otologic surgery are developed. An algorithm for generating appropriate safety margins based on a desired probability of preserving the underlying anatomical structure is presented. Simulations are performed on a CT scan of a cadaver head and safety margins are calculated around several critical structures for planning of a robotic mastoidectomy.

BACKGROUND:When robots mill bone near critical structures, safety margins are used to reduce the risk of accidental damage due to inaccurate registration. These margins are typically set heuristically with uniform thickness, which does not reflect the anisotropy and spatial variance of registration error.METHODS:A method is described to generate spatially varying safety margins around vital anatomy using statistical models of registration uncertainty. Numerical simulations are used to determine the margin geometry that matches a safety threshold specified by the surgeon.RESULTS:The algorithm was applied to CT scans of five temporal bones in the context of mastoidectomy, a common bone milling procedure in ear surgery that must approach vital nerves. Safety margins were generated that satisfied the specified safety levels in every case.CONCLUSIONS:Patient safety in image-guided surgery can be increased by incorporating statistical models of registration uncertainty in the generation of safety margins around vital anatomy.

Robots have been shown to be useful in assisting surgeons in a variety of bone drilling and milling procedures. Examples include commercial systems for joint repair or replacement surgeries, with in vitro feasibility recently shown for mastoidectomy. Typically, the robot is guided along a path planned on a CT image that has been registered to the physical anatomy in the operating room, which is in turn registered to the robot. The registrations often take advantage of the high accuracy of fiducial registration, but, because no real-world registration is perfect, the drill guided by the robot will inevitably deviate from its planned path. The extent of the deviation can vary from point to point along the path because of the spatial variation of target registration error. The allowable deviation can also vary spatially based on the necessary safety margin between the drill tip and various nearby anatomical structures along the path. Knowledge of the expected spatial distribution of registration error can be obtained from theoretical models or experimental measurements and used to modify the planned path. The objective of such modifications is to achieve desired probabilities for sparing specified structures. This approach has previously been studied for drilling straight holes but has not yet been generalized to milling procedures, such as mastoidectomy, in which cavities of more general shapes must be created. In this work, we present a general method for altering any path to achieve specified probabilities for any spatial arrangement of structures to be protected. We validate the method via numerical simulations in the context of mastoidectomy.

Otologic surgery often involves a mastoidectomy, which is the removal of a portion of the mastoid region of the temporal bone, to safely access the middle and inner ear. The surgery is challenging because many critical structures are embedded within the bone, making them difficult to see and requiring a high level of accuracy with the surgical dissection instrument, a high-speed drill. We propose to automate the mastoidectomy portion of the surgery using a compact, bone-attached robot. The system described in this paper is a milling robot with four degrees-of-freedom (DOF) that is fixed to the patient during surgery using a rigid positioning frame screwed into the surface of the bone. The target volume to be removed is manually identified by the surgeon pre-operatively in a computed tomography (CT) scan and converted to a milling path for the robot. The surgeon attaches the robot to the patient in the operating room and monitors the procedure. Several design considerations are discussed in the paper as well as the proposed surgical workflow. The mean targeting error of the system in free space was measured to be 0.5 mm or less at vital structures. Four mastoidectomies were then performed in cadaveric temporal bones, and the error at the edges of the target volume was measured by registering a postoperative computed tomography (CT) to the pre-operative CT. The mean error along the border of the milled cavity was 0.38 mm, and all critical anatomical structures were preserved.

We have developed a new swarm search algorithm for robots based on the trophallactic behavior of social insects. The new algorithm, which we call Particle Search Algorithm (PSA), does not require any bot-bot communication and does not require the bots to have absolute position information. The bots cluster together near peaks in the search space based on a fitness value when bots collide.

This paper describes three new algorithms for dispersing a swarm of bots throughout a search space. We assume that the bots do not have a central coordinating agent and we want to have no (or few) inter-bot communications so that the algorithms can scale to large swarm sizes. We simulated the three new dispersion algorithms plus two other random-walk based dispersion algorithms on five different search spaces. Each of the five algorithms was tested with swarm sizes from three to fifty bots. For swarm sizes larger than ten, we found that the minimize-intensity algorithm, which is based on decaying signal strengths, worked best. For small swarm sizes, the dispersion algorithm based on the dispersion of gas particles performed best.

This paper describes the experimental results of using the particle swarm optimization (PSO) algorithm to control a suite of robots. In our approach, each bot is one particle in the PSO; each particle/bot makes measurements, updates its own position and velocity, updates its own personal best measurement (pbest) and personal best location (if necessary), and broadcasts to the other bots if it has found a global best measurement/position. We built three bots and tested the algorithm by letting the bots find the brightest spot of light in the room. The tests show that using the PSO to control a swarm can successfully find the target, even in the presence of obstacles