The complex 3D geometry of the scapula and the variability among individuals makes it difficult to precisely quantify its morphometric features. Recently, the scapular neck has been recognized as an important morphometric parameter particularly due to the role it plays in scapular notching, which occurs when the humeral component of a reverse shoulder arthroplasty (RSA) prosthesis engages the posterior column of the scapula causing mechanical impingement and osseous wear. Prosthetic design and positioning of the glenoid component have been accepted as two major factors associated with the onset of notching in the RSA patient population. The present image-based study aimed to develop an objective 3D approach of measuring scapular neck, which when measured pre-operatively, may identify individuals at risk for notching. A group of 81 subjects (41 M, 69.7 ± 8.9 yrs.; 40 F, 70.9 ± 8.1 yrs.) treated with RSA were evaluated in this study. The 3D point-cloud of the scapular geometry was obtained from pre-operative computed tomography (CT) scans and rendered in Mimics. Subsequently, a subject-specific glenoid coordinate system was established, using the extracted glenoid surface of each scapula as a coordinate reference. The principal component analysis approach was used to establish three orthogonal coordinate axes in the geometric center of the glenoid. Utilization of glenoid-specific reference planes (glenoid, major axis, and minor axis plane) were selected in order to remove subjectivity in assessing “true” anterior/posterior and profile views of the scapula. The scapular neck length was defined as the orthogonal distance between the glenoid surface and the point on the posterior column with the significant change of curvature (Fig. 1). In addition, the angle between the glenoid plane, area center of the glenoid, and the point of significant change of the curvature were assessed (Fig. 2). This new parameter was developed to serve as a predictive critical value for the occurrence of notching. The incidence of notching increases as the value of the notching angle decreases. In order to evaluate relationships between glenoid and scapular neck, the glenoid width and height was also measured at the glenoid plane.Introduction:
Materials and Methods:
Subchondral bone density (SBD) distribution is an important parameter regarding that may be important when considering implant stability. This parameter is a reflection of the loading experienced by the joint throughout the lifetime and may be useful in pre-surgical planning and implant design. Clinically, the question of the glenoid surface preparation for TSA/RSA remains controversial, despite numerous published studies on glenoid bone morphology. To address this question, there exists a need to develop a 3D quantitative method capable of analyzing the complex glenoid bone morphology at different depths from the surface. Computed tomographic osteoabsoptiomery (CT-OAM) evaluates SBD based on the Housfield Unit (HU) value of each pixel. In this pilot study, we aimed to analyze SBD distribution of the glenoid at different depths by means of CT-OAM in male TSA subjects. A study group of twenty male TSA patients (61–69y.o) were included in this study. Each subject obtained a pre-operative CT scan following a standardized protocol on the same CT scanner (1.25 mm slice thickness). Resultant DICOM 2D images were processed in custom-written program (VC++) and the surface of every glenoid was manually traced from the axial slices. Care was taken during the manual tracing process to exclude osteophytes and cyst formations from the resultant surface. Values of HU at every selected pixel on the surface of the glenoid were recorded. Subsequently, the layer of pixels at a 0.5 mm distance from the previous surface was virtually scraped and the HU values of new layer of pixels were recorded. This routine was repeated up to a depth of 5 mm from the glenoid surface, taking measurements on 11 virtual 3D surfaces with a thickness of 0.5 mm. Mean SBD distribution was reported for each layer and differences were compared using ANOVA and Fisher's post-hoc test.Introduction:
Materials and Methods:
