David F. Short

Computed-tomography-based finite-element models of long bones can accurately capture strain response to bending and torsion

Finite element (FE) models of long bones constructed from computed-tomography (CT) data are emerging as an invaluable tool in the field of bone biomechanics. However, the performance of such FE models is highly dependent on the accurate capture of geometry and appropriate assignment of material properties. In this study, a combined numerical-experimental study is performed comparing FE-predicted surface strains with strain-gauge measurements. Thirty-six major, cadaveric, long bones (humerus, radius, femur and tibia), which cover a wide range of bone sizes, were tested under three-point bending and torsion. The FE models were constructed from trans-axial volumetric CT scans, and the segmented bone images were corrected for partial-volume effects. The material properties (Youngs modulus for cortex, density-modulus relationship for trabecular bone and Poissons ratio) were calibrated by minimizing the error between experiments and simulations among all bones. The R(2) values of the measured strains versus load under three-point bending and torsion were 0.96-0.99 and 0.61-0.99, respectively, for all bones in our dataset. The errors of the calculated FE strains in comparison to those measured using strain gauges in the mechanical tests ranged from -6% to 7% under bending and from -37% to 19% under torsion. The observation of comparatively low errors and high correlations between the FE-predicted strains and the experimental strains, across the various types of bones and loading conditions (bending and torsion), validates our approach to bone segmentation and our choice of material properties.

Fitting of bone mineral density with consideration of anthropometric parameters

SummaryA new model describing normal values of bone mineral density in children has been evaluated, which includes not only the traditional parameters of age, gender, and race, but also weight, height, percent body fat, and sexual maturity. This model may constitute a better comparative norm for a specific child with given anthropometric values.IntroductionPrevious descriptions of children’s bone mineral density (BMD) by age have focused on segmenting diverse populations by race and gender without adjusting for anthropometric variables or have included the effects of anthropometric variables over a relatively homogeneous population.MethodsMultivariate semi-metric smoothing (MS2) provides a way to describe a diverse population using a model that includes multiple effects and their interactions while producing a result that can be smoothed with respect to age in order to provide connected percentiles. We applied MS2 to spine BMD data from the Bone Mineral Density in Childhood Study to evaluate which of gender, race, age, height, weight, percent body fat, and sexual maturity explain variations in the population’s BMD values. By balancing high adjusted R2 values and low mean square errors with clinical needs, a model using age, gender, race, weight, and percent body fat is proposed and examined.ResultsThis model provides narrower distributions and slight shifts of BMD values compared to the traditional model, which includes only age, gender, and race. Thus, the proposed model might constitute a better comparative standard for a specific child with given anthropometric values and should be less dependent on the anthropometric characteristics of the cohort used to devise the model.ConclusionsThe inclusion of multiple explanatory variables in the model, while creating smooth output curves, makes the MS2 method attractive in modeling practically sized data sets. The clinical use of this model by the bone research community has yet to be fully established.

Anthropometric models of bone mineral content and areal bone mineral density based on the bone mineral density in childhood study

SummaryNew models describing anthropometrically adjusted normal values of bone mineral density and content in children have been created for the various measurement sites. The inclusion of multiple explanatory variables in the models provides the opportunity to calculate Z-scores that are adjusted with respect to the relevant anthropometric parameters.IntroductionPrevious descriptions of children’s bone mineral measurements by age have focused on segmenting diverse populations by race and sex without adjusting for anthropometric variables or have included the effects of a single anthropometric variable.MethodsWe applied multivariate semi-metric smoothing to the various pediatric bone-measurement sites using data from the Bone Mineral Density in Childhood Study to evaluate which of sex, race, age, height, weight, percent body fat, and sexual maturity explain variations in the population’s bone mineral values. By balancing high adjusted R2 values with clinical needs, two models are examined.ResultsAt the spine, whole body, whole body sub head, total hip, hip neck, and forearm sites, models were created using sex, race, age, height, and weight as well as an additional set of models containing these anthropometric variables and percent body fat. For bone mineral density, weight is more important than percent body fat, which is more important than height. For bone mineral content, the order varied by site with body fat being the weakest component. Including more anthropometrics in the model reduces the overlap of the critical groups, identified as those individuals with a Z-score below −2, from the standard sex, race, and age model.ConclusionsIf body fat is not available, the simpler model including height and weight should be used. The inclusion of multiple explanatory variables in the models provides the opportunity to calculate Z-scores that are adjusted with respect to the relevant anthropometric parameters.

Development of quantitative computed-tomography-based strength indicators for the identification of low bone-strength individuals in a clinical environment☆ , ☆☆

The aim of this study was to develop quantitative computed-tomography (QCT)-based bone-strength indicators that highly correlate with finite-element (FE)-based strength. Transaxial QCT scans were obtained from 36 major, cadaveric, long bones (humerus, radius, femur and tibia) from 4 females and 2 males, 53 to 86 years old. These images were used to construct the FE models and to develop the QCT-based bone strength indicators under every-day, simplified loading conditions. We have evaluated the performance of area-weighted (AW), density-weighted (DW) and modulus-weighted (MW) rigidity measures as well as popular strength indicators like section modulus (Z) and stress-strain index (SSI). We have also developed a novel strength metric, the centroid deviation, which analyzes the spatial distribution of the centroids along the length of the bone. The correlation results show that the MW polar moment of inertia and the MW moment of inertia are the two top-performers for all bones and loading conditions (average r>0.89). The MW centroid deviations correlated highly with the estimated load to fracture for all bones under compression (r>0.83), except for the humerus (r=0.67). Consistently DW or MW rigidity measures produced a statistically significant improvement in capturing bone strength compared to AW rigidity measures. As expected, MW rigidity measures showed a higher correlation with the FE-based fracture load than the DW rigidity measures; however, the improvement was not statistically significant. Through this study we present a short-list of useful QCT-based strength parameters that correlate well with FE-based fracture load. Although a few parameters perform reasonably well across most bones and loading conditions, a judicious assessment of bone strength should include multiple parameters evaluated at multiple critical locations in the long bones, with attention to the type of loading and bone type.

High-performance computing strategies for SAR image experiments

This article presents different strategies for generating very large sets of SAR phase history and imagery for target recognition studies using the open-use Raider Tracer simulation tool. Previous data domes, based on Visual D, produced numerous data sets for ground targets above a flat surface, but each target had a single orientation. Here, the experiment specifies different target types, each above a ground plane, but with arbitrary pose, yaw, and pitch. The customized data set poses challenges to load balancing and file input/output synchronization for a limited cpu hour budget. Strategies are presented to complete each image within a minimal time, and to generate the complete experiment set within a desired time.

Performance Analysis of Quantitative Bone Measurement with Spiral CT

The goal of this study was to evaluate a number of modern multi-slice helical CT scanners with respect to random and systematic errors in the measurement of bone density. For this purpose, a rod phantom was designed to mimic a patient with various amounts of bone, and scans of the rod phantom along with the Mindways calibration phantom were taken using General Electric, Siemens and Toshiba 4-, 16- and 64-slice CT scanners.

Repositioning of the Region of Interest in the Radius of the Growing Child in Follow-Up Measurements by pQCT

The assessment of changes in bone mineral density (BMD) requires follow-up measurements. In order to compute accurate changes, it is important that the region of interest (ROI) of the initial and follow-up measurements match in terms of location. Our present focus is the evaluation of bone at the distal end of the radius by peripheral quantitative computed tomography (pQCT). In adults, in whom the bones have ceased to grow, repositioning of the ROI has been attempted either by matching the measurement location defined as a fixed percentage of the bone length away from an anatomical landmark or by matching the cross-sectional areas of the bone in the conic distal region of the radius; however, in the case of children, whose bones are growing, these methods cannot be applied blindly. The aim of this study was to propose a model that aids in the relocation of the ROI during follow-up measurements in children.