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Evaluation of the bony anatomy of the glenohumeral joint is frequently required for surgical planning and subject-specific computational modeling and simulation. The three-dimensional geometry of bones is traditionally obtained by segmenting medical image datasets, but this can be time-consuming and may not be practical in the clinical setting. The aims of this study were twofold. Firstly, to develop and validate a statistical shape modeling approach to rapidly reconstruct the complete scapular and humeral geometries using discrete morphometric measurements that can be quickly and easily measured directly from CT, and secondly, to assess the effectiveness of statistical shape modeling in reconstruction of the entire humerus using just the landmarks in the immediate vicinity of the glenohumeral joint. The most representative shape prediction models presented in this study achieved complete scapular and humeral geometry prediction from seven or fewer morphometric measurements and yielded a mean surface root mean square (RMS) error under 2 mm. Reconstruction of the entire humerus was achieved using information of only proximal humerus bony landmarks and yielding mean surface RMS errors under 3 mm. The proposed statistical shape modeling facilitates rapid generation of 3D anatomical models of the shoulder, which may be useful in rapid development of personalized musculoskeletal models.

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

Despite evidence of the biomechanical role of cortical bone, current state of the art finite element models of the proximal femur built from clinical CT data lack a subject-specific representation of the bone cortex. Our main research hypothesis is that the subject-specific modelling of cortical bone layer from CT images, through a deconvolution procedure known as Cortical Bone Mapping (CBM, validated for cortical thickness and density estimates) can improve the accuracy of CT-based FE models of the proximal femur, currently limited by partial volume artefacts. Our secondary hypothesis is that a careful choice of cortical-specific density-elasticity relationship may improve model accuracy. We therefore: (i) implemented a procedure to include subject-specific CBM estimates of both cortical thickness and density in CT-based FE models. (ii) defined alternative models that included CBM estimates and featured a cortical-specific or an independently optimised density-elasticity relationship. (iii) tested our hypotheses in terms of elastic strain estimates and failure load and location prediction, by comparing with a published cohort of 14 femurs, where strain and strength in stance and fall loading configuration were experimentally measured, and estimated through reference FE models that did not explicitly model the cortical compartment. Our findings support the main hypothesis: an explicit modelling of the proximal femur cortical bone layer including CBM estimates of cortical bone thickness and density increased the FE strains prediction, mostly by reducing peak errors (average error reduced by 30%, maximum error and 95th percentile of error distribution halved) and especially when focusing on the femoral neck locations (all error metrics at least halved). We instead rejected the secondary hypothesis: changes in cortical density-elasticity relationship could not improve validation performances. From these improved baseline strain estimates, further work is needed to achieve accurate strength predictions, as models incorporating cortical thickness and density produced worse estimates of failure load and equivalent estimates of failure location when compared to reference models. In summary, we recommend including local estimates of cortical thickness and density in FE models to estimate bone strains in physiological conditions, and especially when designing exercise studies to promote bone strength. ispartof: BONE vol:136 ispartof: location:United States status: published

This article outlines the methods and applications of threshold-based techniques to assess in vivo muscle and bone tissue distribution in normal and pathological conditions using computed tomography imaging. The approaches described here use medical imaging processing techniques and computational methods to study bone mechanical proprieties, analyze and quantify muscle morphology, visualize changes with 3-D models, develop subject-specific numerical profiles, and assess muscle and bone changes during clinical treatments.