Toward Practical and Accurate Touch-Based Image Guidance for 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.

Cadaveric Testing of Robot-Assisted Access to the Internal Auditory Canal for Vestibular Schwannoma Removal.

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.

Safety margins in robotic bone milling: from registration uncertainty to statistically safe surgeries.

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.

Through the Eustachian Tube and Beyond: A New Miniature Robotic Endoscope to See Into The Middle Ear.

This paper presents a novel miniature robotic endoscope that is small enough to pass through the Eustachian tube and provide 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 paper, 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 3D-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, non-articulated endoscope through the Eustachian Tube.

Increasing Safety of a Robotic System for Inner Ear Surgery Using Probabilistic Error Modeling Near Vital Anatomy.

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.

Incorporating Target Registration Error Into Robotic Bone Milling.

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.

A Compact, Bone-Attached Robot for 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.