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Technical note: known-component registration for robotic drill guide positioning
Author(s) -
Thomas Yi,
Vignesh Ramchandran,
Jeffrey H. Siewerdsen,
Ali Uneri
Publication year - 2018
Publication title -
pubmed central
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1117/12.2322408
Subject(s) - drill , imaging phantom , computer vision , cadaver , artificial intelligence , image registration , computer science , radiography , fiducial marker , image guided surgery , patient registration , medicine , nuclear medicine , radiology , surgery , engineering , image (mathematics) , mechanical engineering
A method for x-ray-guided robotic positioning of surgical instruments is reported and evaluated in preclinical studies of spine pedicle screw placement with the aim of improving delivery of transpedicle drills and screws. The known-component registration (KC-Reg) algorithm was used to register the 3D patient CT and the surface model of a drill guide to intraoperatively acquired 2D radiographs. Resulting transformations, combined with offline hand-eye calibration, drive a robotically-held drill guide to target trajectories established in the preoperative patient CT. The proposed method was assessed against more conventional surgical tracker guidance, and robustness to clinically realistic errors was tested in phantom and cadaver studies. Target registration error (TRE) was computed as drill guide deviation from the planned trajectory. The KC-Reg approach resulted in 1.51 ± 0.51 mm error at tooltip and 1.01 ± 0.92° in approach angle, showing comparable performance to the tracker-guided approach. In cadaver studies with anatomical deformation, TRE of 2.31 ± 1.05 mm and 0.66 ± 0.62° were observed, with statistically improved performance over a surgical tracker through registration of locally rigid bony anatomy. X-ray guidance offers an accurate means of driving robotic systems that is compatible with conventional fluoroscopic workflow. Specifically, such procedures involve multi-planar fluoroscopic views that are qualitatively interpreted by the surgeon; the KC-Reg approach accomplishes this using the same multi-planar views to provide greater quantitative accuracy and valuable guidance and QA. The method was robust against anatomical deformation due to the radiographic scene’s local nature used in registration, presenting a potentially major surgical benefit.

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