A calibration-free workflow for image-based mixed reality navigation of total shoulder arthroplasty. Issue 3 (4th May 2022)

• Record Type: Journal Article
• Authors: Gu, Wenhao; Shah, Kinjal; Knopf, Jonathan; Josewski, Chad; Unberath, Mathias
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 10:Issue 3(2022)
• Page Start: 243
• Page End: 251
• Publication Date: 2022-05-04
• Subjects: Augmented reality -- joint replacement -- registration -- surgical navigation -- deep learning -- transformer; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; 616.0757
• DOI: 10.1080/21681163.2021.2009378
• Languages: English
• ISSNs: 2168-1163

Abstract

ABSTRACT: Accurate pin placement to guide renaming of the glenoid surface in total shoulder arthroplasty (TSA) is a critical step to restore range of motion in the glenohumeral joint. Achieving proper pin position with free-hand is complicated due to inadequate intra-operative availability of pre-operative planning data. Mixed reality provides a new modality of surgical navigation for arthroplasty without distracting surgeon from the patient anatomy. However, achieving accurate alignment between the pre-operative plan and the intra-operative anatomy is challenging, especially if workflow-compliant solutions are desired. In this paper, we present a calibration-free mixed reality navigation workflow for glenoid pin placement in TSA that contributes to solving some open challenges. We use a visible light stereo camera on a surgical support arm combined with a transformer-based disparity estimation algorithm to accurately reconstruct the 3D anatomy despite surgical lighting. Besides, we introduce a slide-on marker calibrated by design to overcome perceptual difficulties that arise when trying to align real to virtual objects. In a user study on a semi-anthropomorphic phantom, we assess the end-to-end pin placement accuracy using both our system and a clinical reference standard solution that relies on patient-specific drill guides. Averaging over 15 drilled trajectories, our mixed reality system achieved a pin placement accuracy of ( 3.80 ± 1.28 mm, 4.66 ± 2.85 ° ).