Quantitative SSM-based analysis of humeral head migration in rotator cuff tear arthropathy patients.

Rotator cuff tear arthropathy (RCTA) is characterized by massive rotator cuff tearing combined with humeral head migration (HHM). The aim of this study is to investigate the quantitative characteristics of this migration and its association with glenoid erosions and prearthropathy scapular anatomy. We quantified HHM and prearthropathy scapular anatomy of 64 RCTA patients with statistical shape modeling-based techniques. Glenoid erosion was classified according to Sirveaux et al. A cutoff value for confirming HHM was 5 mm based on a control group of 49 patients. Group 1 (RCTA without HHM) consisted of 21 patients, with a mean subluxation distance (SLD) of 3 mm. Group 2 (RCTA with HHM) consisted of 43 patients, with mean SLD of 9 mm, SLD in the anteroposterior plane of -1 mm (SD ± 4 mm), SLD in the superoinferior plane of 7 mm (SD ± 3 mm), and subluxation angle (SLA) of -5° (SD ± 40°). Analysis with Fisher's exact test showed a clear association between HHM and glenoid erosions (p = 0.002). Multivariate regression analysis of Group 2 showed that prearthropathy lateral acromial angle combined with critical shoulder angle (p = 0.004) explained 21% of the observed variability in SLD. The prearthropathy glenoid version explained 23% of the variability in SLA (p = 0.001). HHM in RCTA patients has a wide variation in both magnitude and direction leading to a distorted glenohumeral relationship in the coronal and axial plane. HHM is highly associated with the occurrence of glenoid erosions. There is a correlation between the prearthropathy scapular anatomy and the magnitude and direction of HHM.

Automated muscle elongation measurement during reverse shoulder arthroplasty planning.

BACKGROUND: Adequate deltoid and rotator cuff elongation in reverse shoulder arthroplasty is crucial to maximize postoperative functional outcomes and to avoid complications. Measurements of deltoid and rotator cuff elongation during preoperative planning can support surgeons in selecting a suitable implant design and position. Therefore, this study presented and evaluated a fully automated method for measuring deltoid and rotator cuff elongation. METHODS: Complete scapular and humeral models were extracted from computed tomography scans of 40 subjects. First, a statistical shape model of the complete humerus was created and evaluated to identify the muscle attachment points. Next, a muscle wrapping algorithm was developed to identify the muscle paths and to compute muscle lengths and elongations after reverse shoulder arthroplasty implantation. The accuracy of the muscle attachment points and the muscle elongation measurements was evaluated for the 40 subjects by use of both complete and artificially created partial humeral models. Additionally, the muscle elongation measurements were evaluated for a set of 50 arthritic shoulder joints. Finally, a sensitivity analysis was performed to evaluate the impact of implant positioning on deltoid and rotator cuff elongation. RESULTS: For the complete humeral models, all muscle attachment points were identified with a median error < 3.5 mm. For the partial humeral models, the errors on the deltoid attachment point largely increased. Furthermore, all muscle elongation measurements showed an error < 1 mm for 75% of the subjects for both the complete and partial humeral models. For the arthritic shoulder joints, the errors on the muscle elongation measurements were <2 mm for 75% of the subjects. Finally, the sensitivity analysis showed that muscle elongations were affected by implant positioning. DISCUSSION: This study presents an automated method for accurately measuring muscle elongations during preoperative planning of shoulder arthroplasty. The results show that the accuracy in measuring muscle elongations is higher than the accuracy in indicating the muscle attachment points. Hence, muscle elongation measurements are insensitive to the observed errors on the muscle attachment points. Related to this finding, muscle elongations can be accurately measured for both a complete humeral model and a partial humeral model. Because the presented method also showed accurate results for arthritic shoulder joints, it can be used during preoperative shoulder arthroplasty planning, in which typically only the proximal humerus is present in the scan and in which bone arthropathy can be present. As the muscle elongations are sensitive to implant positioning, surgeons can use the muscle elongation measurements to refine their surgical plan.

Automated quantification of glenoid bone defects using 3-dimensional measurements.

BACKGROUND: Assessment of glenoid bone defects is important to select the optimal glenoid component design during shoulder arthroplasty planning and implantation. This study presents a fully automated method to describe glenoid bone loss using 3-dimensional measurements without the need for a healthy contralateral reference scapula. METHODS: The native shape of the glenoid is reconstructed by fitting a statistical shape model (SSM) of the scapula. The total vault loss percentage, local vault loss percentages, defect depth, defect area percentage, and subluxation distance and region are computed based on a comparison of the reconstructed and eroded glenoids. The method is evaluated by comparing its results with a contralateral bone-based reconstruction approach in a data set of 34 scapula and humerus pairs with unilateral glenoid bone defects. RESULTS: The SSM-based defect measurements deviated from the contralateral bone-based measurements with mean absolute differences of 5.5% in the total vault loss percentage, 4.5% to 8.0% in the local vault loss percentages, 1.9 mm in the defect depth, 14.8% in the defect area percentage, and 1.6 mm in the subluxation distance. The SSM-based method was statistically equivalent to the contralateral bone-based method for all parameters except the defect area percentage. CONCLUSION: The presented method is able to automatically analyze glenoid bone defects using 3-dimensional measurements without the need for a healthy contralateral bone.

Can the contralateral scapula be used as a reliable template to reconstruct the eroded scapula during shoulder arthroplasty?

HYPOTHESIS: The contralateral scapula can be used as a reliable template to determine scapular offset, glenoid inclination, and version of the native scapula in view of reconstructing pathologic scapulae. METHODS: Three-dimensional measurements of scapular offset, inclination, and version were performed using data from a set of 50 bilateral computed tomography scans of full scapulae to determine direct side-to-side differences. RESULTS: The scapula pairs had a mean bilateral difference of 2 mm in offset, 2° in inclination, and 2° in version. Ninety percent of the scapula pairs showed an offset difference smaller than 3 mm. In 96% and 94% of the scapula pairs, the inclination difference and version difference, respectively, were smaller than 5°. The maximum bilateral difference for offset, inclination, and version was 6 mm, 6°, and 8°, respectively. DISCUSSION AND CONCLUSION: The anatomic parameters of scapular offset, glenoid inclination, and version are quite symmetrical and fall into the currently technically feasible accuracy of shoulder arthroplasty implantation. The healthy scapula can be used as a template to guide the reconstruction of the glenoid during shoulder arthroplasty planning in the case of unilateral advanced arthropathy.

Virtual reconstruction of glenoid bone defects using a statistical shape model.

BACKGROUND: Description of the native shape of a glenoid helps surgeons to preoperatively plan the position of a shoulder implant. A statistical shape model (SSM) can be used to virtually reconstruct a glenoid bone defect and to predict the inclination, version, and center position of the native glenoid. An SSM-based reconstruction method has already been developed for acetabular bone reconstruction. The goal of this study was to evaluate the SSM-based method for the reconstruction of glenoid bone defects and the prediction of native anatomic parameters. METHODS: First, an SSM was created on the basis of 66 healthy scapulae. Then, artificial bone defects were created in all scapulae and reconstructed using the SSM-based reconstruction method. For each bone defect, the reconstructed surface was compared with the original surface. Furthermore, the inclination, version, and glenoid center point of the reconstructed surface were compared with the original parameters of each scapula. RESULTS: For small glenoid bone defects, the healthy surface of the glenoid was reconstructed with a root mean square error of 1.2 ± 0.4 mm. Inclination, version, and glenoid center point were predicted with an accuracy of 2.4° ± 2.1°, 2.9° ± 2.2°, and 1.8 ± 0.8 mm, respectively. DISCUSSION AND CONCLUSION: The SSM-based reconstruction method is able to accurately reconstruct the native glenoid surface and to predict the native anatomic parameters. Based on this outcome, statistical shape modeling can be considered a successful technique for use in the preoperative planning of shoulder arthroplasty.