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- W1967882559 abstract "Image-guided surgery (IGS) allows clinicians to view current, intra-operative scenes superimposed on preoperativeimages (typically MRI or CT scans). IGS systems use localization systems to track and visualize surgical tools overlaidon top of preoperative images of the patient during surgery. The most commonly used localization systems in theOperating Rooms (OR) are optical tracking systems (OTS) due to their ease of use and cost effectiveness. However,OTS' suffer from the major drawback of line-of-sight requirements. State space approaches based on differentimplementations of the Kalman filter have recently been investigated in order to compensate for short line-of-sightocclusion. However, the proposed parameterizations for the rigid body orientation suffer from singularities at certainvalues of rotation angles. The purpose of this work is to develop a quaternion-based Unscented Kalman Filter (UKF) forrobust optical tracking of both position and orientation of surgical tools in order to compensate marker occlusion issues.This paper presents preliminary results towards a Kalman-based Sensor Management Engine (SME). The engine willfilter and fuse multimodal tracking streams of data. This work was motivated by our experience working in robot-basedapplications for keyhole neurosurgery (ROBOCAST project). The algorithm was evaluated using real data from NDIPolaris tracker. The results show that our estimation technique is able to compensate for marker occlusion with amaximum error of 2.5° for orientation and 2.36 mm for position. The proposed approach will be useful in over-crowdedstate-of-the-art ORs where achieving continuous visibility of all tracked objects will be difficult." @default.
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- W1967882559 date "2012-02-23" @default.
- W1967882559 modified "2023-09-23" @default.
- W1967882559 title "Application of unscented Kalman filter for robust pose estimation in image-guided surgery" @default.
- W1967882559 doi "https://doi.org/10.1117/12.912165" @default.
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