Robotic drill guide positioning using known-component 3D–2D image registration

Abstract

A method for x-ray image-guided robotic instrument positioning is reported and evaluated in preclinical studies of spinal pedicle screw placement with the aim of improving delivery of transpedicle K-wires and screws. The known-component (KC) registration algorithm was used to register the three-dimensional patient CT and drill guide surface model to intraoperative two-dimensional radiographs. Resulting transformations, combined with offline hand-eye calibration, drive the robotically held drill guide to target trajectories defined in the preoperative CT. The method was assessed in comparison with a more conventional tracker-based approach, and robustness to clinically realistic errors was tested in phantom and cadaver. Deviations from planned trajectories were analyzed in terms of target registration error (TRE) at the tooltip (mm) and approach angle (deg). In phantom studies, the KC approach resulted in [Formula: see text] and [Formula: see text], comparable with accuracy in tracker-based approach. In cadaver studies with realistic anatomical deformation, the KC approach yielded [Formula: see text] and [Formula: see text], with statistically significant improvement versus tracker ([Formula: see text] and [Formula: see text]). Robustness to deformation is attributed to relatively local rigidity of anatomy in radiographic views. X-ray guidance offered accurate robotic positioning and could fit naturally within clinical workflow of fluoroscopically guided procedures.

A mobile isocentric C‐arm for intraoperative cone‐beam CT: Technical assessment of dose and 3D imaging performance

“…In each of these areas, CBCT provides a basis for surgical planning, quality assurance (QA), and improved patient safety . Integration with three‐dimensional (3D)‐3D image registration (CBCT to preoperative CT or MRI), 3D‐2D image registration (CBCT to radiographs), and surgical robotics are active areas of translational development …”

“…[7][8][9] Integration with three-dimensional (3D)-3D image registration (CBCT to preoperative CT or MRI), 10 3D-2D image registration (CBCT to radiographs), 11 and surgical robotics are active areas of translational development. 12,13 Mobile C-arms with CBCT capability using x-ray image intensifiers (XRIIs) have been available for many years (e.g., Arcadis Orbic 3D, Siemens Healthcare) but exhibit major limitations in image quality owing to limited field of view, image distortion, spatial resolution, and noise. Many of these image quality limitations are remedied by systems employing a flat-panel detector (FPD)for example, mobile systems such as the O-arm (Medtronic, Littleton MA) and Vision 3D C-arm (Ziehm, Nuremberg, Germany).…”

Automated Polyaxial Screw Placement Using a Commercial-Robot-Based, Image-Guided Spine Surgery System

“…Whereas the robot used its own internal localization capability for navigation, the system is currently unable to track patient movement, a critical feature for a clinical system. While a navigation system could be integrated, a fixed x-ray system could also be used to detect movement and correct the registration [38]. It would also be straightforward to obviate the need to implant and touch fiducial markers by having the robot hold a radiopaque fiducial marker pattern such that it was included in an intraoperative image, as is done with the commercial robotic spine surgery systems.…”

Hands‐on robot‐assisted fracture reduction system guided by a linear guidance constraints controller using a pre‐operatively planned goal pose

“…Currently, many research groups have been trying to improve the accuracy of registration. 30,31 We expect that if the accuracy increases more, the precision of the LGC controller will also be increased.…”