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.
[1]
Russell H. Taylor,et al.
Medical robotics in computer-integrated surgery
,
2003,
IEEE Trans. Robotics Autom..
[2]
Jürgen Weese,et al.
A comparison of similarity measures for use in 2-D-3-D medical image registration
,
1998,
IEEE Transactions on Medical Imaging.
[3]
F Perna,et al.
Pedicle screw insertion techniques: an update and review of the literature
,
2016,
MUSCULOSKELETAL SURGERY.
[4]
Mili Shah,et al.
An overview of robot-sensor calibration methods for evaluation of perception systems
,
2012,
PerMIS.
[5]
Jeffrey H Siewerdsen,et al.
Robotic drill guide positioning using known-component 3D–2D image registration
,
2018,
Journal of medical imaging.
[6]
A Uneri,et al.
Known-component 3D–2D registration for quality assurance of spine surgery pedicle screw placement
,
2015,
Physics in medicine and biology.
[7]
A Uneri,et al.
Intraoperative evaluation of device placement in spine surgery using known-component 3D–2D image registration
,
2017,
Physics in medicine and biology.