Bullseye: preclinical evaluation of a novel structured light-based imaging workflow to inform glenoid implant positioning in total shoulder arthroplasty

Abstract

BackgroundAccurate glenoid component positioning during total shoulder arthroplasty (TSA) is critical for prosthesis longevity and postoperative function. Glenoid component positioning in many TSA procedures depends on the insertion of a guide pin through the glenoid vault. However, up to 48% of TSA procedures involve guide pin malpositioning. The aim of this study was to evaluate the ability of a novel structured light imaging system to visualize glenoid guide pin position and trajectory in surgically exposed cadaveric shoulders. Computed tomography (CT)-based and magnetic resonance imaging (MRI)-based workflows and subchondral bone–based and glenoid cartilage–based workflows were compared. MethodsPreoperative cone-beam CT (CBCT) and MRI images were acquired for 5 intact cadaveric shoulders. Following deltopectoral surgical exposure, a glenoid vault guide pin was inserted through the glenoid vault of each scapula as in a clinical TSA procedure. A 3D printed optical tracker was placed over the guide pin, and a 3D optical surface image of the glenoid and tracker was acquired using a handheld structured light sensor. A postprocedural CBCT was acquired for each shoulder to verify guide pin position and trajectory. The imaging procedure was repeated after débridement of the glenoid cartilage to expose subchondral bone. The guide pin was segmented from the postprocedural CBCT image (actual guide pin). A virtual model of the tracker was aligned with a co-linear representation of the intraoperative guide pin (predicted guide pin). A series of image registrations aligned the actual and predicted guide pin positions to yield visualization accuracy, defined as the trajectory and offset errors between predicted and actual guide pins. ResultsThe mean guide pin trajectory and offset errors based on the subchondral bone were 2.22 ± 1.27° and 1.27 ± 0.46 mm for the CT-based workflow and 2.27 ± 1.72° and 1.78 ± 0.92 mm for the MRI-based workflow, respectively. Registration of the cartilage surface models visualized in the MRI images reduced accuracy to a trajectory error of 3.89 ± 1.57° (P = .147) and offset error of 2.28 ± 1.33 mm (P = .217). ConclusionThe Bullseye structured light imaging system presented an accurate approach for glenoid guide pin verification and adjustment during TSA using preoperative MRI or CT. Future development for the implementation of the Bullseye system should focus on improving surface segmentations and automation of the computer vision algorithm needed to facilitate clinical translation.