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.
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.
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.
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