An in silico parametric model of vertebrae trabecular bone based on density and microstructural parameters to assess risk of fracture in osteoporosis.

Osteoporosis is a progressive bone disease characterized by deterioration in the quantity and quality of bone, leading to inferior mechanical properties and an increased risk of fracture. Current assessment of osteoporosis is typically based on bone densitometry tools such as Quantitative Computed Tomography (QCT) and Dual Energy X-ray absorptiometry (DEXA). These assessment modalities mainly rely on estimating the bone mineral density (BMD). Hence present densitometry tools describe only the deterioration of the quantity of bone associated with the disease and not the affected morphology or microstructural changes, resulting in potential incomplete assessment, many undetected patients, and unexplained fractures. In this study, an in-silico parametric model of vertebral trabecular bone incorporating both material and microstructural parameters was developed towards the accurate assessment of osteoporosis and the consequent risk of bone fracture. The model confirms that the mechanical properties such as strength and stiffness of vertebral trabecular tissue are highly influenced by material properties as well as morphology characteristics such as connectivity, which reflects the quality of connected inter-trabecular parts. The FE cellular solid model presented here provides a holistic approach that incorporates both material and microstructural elements associated with the degenerative process, and hence has the potential to provide clinical practitioners and researchers with more accurate assessment method for the degenerative changes leading to inferior mechanical properties and increased fracture risk associated with age and/or disease such as Osteoporosis.

Assessment of forward head posture in females: observational and photogrammetry methods.

BACKGROUND: There are different methods to assess forward head posture (FHP) but the accuracy and discrimination ability of these methods are not clear. OBJECTIVES: Here, we want to compare three postural angles for FHP assessment and also study the discrimination accuracy of three photogrammetric methods to differentiate groups categorized based on observational method. METHOD: All Seventy-eight healthy female participants (23 ± 2.63 years), were classified into three groups: moderate-severe FHP, slight FHP and non FHP based on observational postural assessment rules. Applying three photogrammetric methods - craniovertebral angle, head title angle and head position angle - to measure FHP objectively. RESULTS: One - way ANOVA test showed a significant difference in three categorized group's craniovertebral angle (P< 0.05, F=83.07). There was no dramatic difference in head tilt angle and head position angle methods in three groups. According to Linear Discriminate Analysis (LDA) results, the canonical discriminant function (Wilks'Lambda) was 0.311 for craniovertebral angle with 79.5% of cross-validated grouped cases correctly classified. CONCLUSION: Our results showed that, craniovertebral angle method may discriminate the females with moderate-severe and non FHP more accurate than head position angle and head tilt angle. The photogrammetric method had excellent inter and intra rater reliability to assess the head and cervical posture.

On prediction of the strength levels and failure patterns of human vertebrae using quantitative computed tomography (QCT)-based finite element method.

This paper presents an effective patient-specific approach for prediction of failure initiation and growth in human vertebra using the general framework of the quantitative computed tomography (QCT)-based finite element method (FEM). The studies were carried out on 13 vertebrae (lumbar and thoracic), excised from 3 cadavers with the average age of 42 years old. Initially, 4 samples were QCT scanned and the images were directly converted into voxel-based 3D finite element models for linear and nonlinear analyses. The equivalent plastic strains obtained from the nonlinear analyses were used to predict the occurrence of local failures and development of the failure patterns. In the linear analyses, the strain energy density measure was used to identify the critical elements and predict the failure patterns. Subsequently, the samples were destructively tested in uniaxial compression and the experimental load-displacement diagrams were obtained. The plain radiographic images of the tested samples were also examined for observation of the failure patterns. In continuation, the presence of osteolytic defects in vertebrae was simulated by creation of artificial cavities within 9 remaining samples using a computer numerical control (CNC) milling machine. The same protocol was followed for scanning, modeling, and destructive testing of these samples. A strong correlation was found between the predicted and measured strengths. Finally, a typical vertebroplasty treatment was simulated by injection of low-viscosity bone cement within 3 compressed samples. The failure patterns and the associated load levels for these samples were also predicted using the QCT voxel-based FEM.