Eva-Maria Lochmüller

Paracelsus Medical University Salzburg, Salzburg, Salzburg, Austria

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Publications (38)98.04 Total impact

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    ABSTRACT: The ability of Anisotropic Minkowski Functionals (AMFs) to capture local anisotropy while evaluating topological properties of the underlying gray-level structures has been previously demonstrated. We evaluate the ability of this approach to characterize local structure properties of trabecular bone micro-architecture in ex vivo proximal femur specimens, as visualized on multi-detector CT, for purposes of biomechanical bone strength prediction. To this end, volumetric AMFs were computed locally for each voxel of volumes of interest (VOI) extracted from the femoral head of 146 specimens. The local anisotropy captured by such AMFs was quantified using a fractional anisotropy measure; the magnitude and direction of anisotropy at every pixel was stored in histograms that served as a feature vectors that characterized the VOIs. A linear multi-regression analysis algorithm was used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction performance was obtained from the fractional anisotropy histogram of AMF Euler Characteristic (RMSE = 1.01 ± 0.13), which was significantly better than MDCT-derived mean BMD (RMSE = 1.12 ± 0.16, p<0.05). We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding regional trabecular bone quality and contribute to improved bone strength prediction, which is important for improving the clinical assessment of osteoporotic fracture risk.
    03/2014;
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    ABSTRACT: Regional trabecular bone quality estimation for purposes of femoral bone strength prediction is important for improving the clinical assessment of osteoporotic fracture risk. In this study, we explore the ability of 3D Minkowski Functionals derived from multi-detector computed tomography (MDCT) images of proximal femur specimens in predicting their corresponding biomechanical strength. MDCT scans were acquired for 50 proximal femur specimens harvested from human cadavers. An automated volume of interest (VOI)-fitting algorithm was used to define a consistent volume in the femoral head of each specimen. In these VOIs, the trabecular bone micro-architecture was characterized by statistical moments of its BMD distribution and by topological features derived from Minkowski Functionals. A linear multiregression analysis and a support vector regression (SVR) algorithm with a linear kernel were used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction result was obtained from the Minkowski Functional surface used in combination with SVR, which had the lowest prediction error (RMSE = 0.939 ± 0.345) and which was significantly lower than mean BMD (RMSE = 1.075 ± 0.279, p<0.005). Our results indicate that the biomechanical strength prediction can be significantly improved in proximal femur specimens with Minkowski Functionals extracted from on MDCT images used in conjunction with support vector regression.
    02/2014;
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    ABSTRACT: PURPOSE Biomechanical bone strength prediction in proximal femur is important for osteoporosis diagnosis and fracture risk estimation. Our study proposes using advanced geometrical scaling index bone structure characterization in combination with statistical bone mineral density (BMD) features extracted from multi-detector computed tomography (MDCT) images of proximal femur specimens, with subsequent prediction of bone strength through support vector regression (SVR). The performance of this system is compared with a standard approach that uses mean BMD and multi-regression models. METHOD AND MATERIALS Axial MDCT images were acquired from 146 proximal femur specimens using a 16-row scanner and a calibration phantom. Adaptive spherical volumes of interest (VOI) were positioned in the femoral head (Huber et al., Radiology 2008) for BMD conversion and image analysis. VOIs of these BMD images were characterized through statistical moments as well as advanced geometrical features extracted with the Scaling Index Method (SIM) (Huber et al., IEEE-TBME 2011). The specimens were then biomechanically tested through a lateral fall on the greater trochanter, and failure load was recorded. All features were analyzed by multi-regression and SVR for predicting bone strength. The performance for different combinations of feature groups was compared using root-mean-square error (RMSE) and coefficient of determination (R2). A Wilcoxon signed-rank test was used to compare two RMSE distributions and test for statistically significant differences in performance. RESULTS Combination of SIM features and mean BMD, when used in conjunction with SVR, exhibited the best prediction performance (RMSE = 0.95 ± 0.13; R2 = 0.62). This was significantly better than the standard approach of using BMD and multi-regression (RMSE = 1.11 ± 0.141; R2 = 0.490). CONCLUSION Our results show that the performance of predicting biomechanical strength in proximal femurs can be significantly improved by including SIM-derived geometrical features in addition to mean BMD, and through the use of support vector regression. CLINICAL RELEVANCE/APPLICATION Complementing BMD characterization on MDCT images with advanced geometrical features and machine learning can contribute to improved osteoporosis diagnosis and disease progression monitoring.
    Radiological Society of North America 2013 Scientific Assembly and Annual Meeting; 12/2013
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    ABSTRACT: In this study, we investigated the scaling relations between trabecular bone volume fraction (BV/TV) and parameters of the trabecular microstructure at different skeletal sites. Cylindrical bone samples with a diameter of 8 mm were harvested from different skeletal sites of 154 human donors in vitro: 87 from the distal radius, 59/69 from the thoracic/lumbar spine, 51 from the femoral neck, and 83 from the greater trochanter. μCT images were obtained with an isotropic spatial resolution of 26 μm. BV/TV and trabecular microstructure parameters (TbN, TbTh, TbSp, scaling indices (< > and σ of α and αz), and Minkowski Functionals (Surface, Curvature, Euler)) were computed for each sample. The regression coefficient β was determined for each skeletal site as the slope of a linear fit in the double-logarithmic representations of the correlations of BV/TV versus the respective microstructure parameter. Statistically significant correlation coefficients ranging from r=0.36 to r=0.97 were observed for BV/TV versus microstructure parameters, except for Curvature and Euler. The regression coefficients β were 0.19 to 0.23 (TbN), 0.21 to 0.30 (TbTh), -0.28 to -0.24 (TbSp), 0.58 to 0.71 (Surface) and 0.12 to 0.16 (<α>), 0.07 to 0.11 (<αz>), -0.44 to -0.30 (σ(α)), and -0.39 to -0.14 (σ(αz)) at the different skeletal sites. The 95% confidence intervals of β overlapped for almost all microstructure parameters at the different skeletal sites. The scaling relations were independent of vertebral fracture status and similar for subjects aged 60-69, 70-79, and >79 years. In conclusion, the bone volume fraction-microstructure scaling relations showed a rather universal character.
    Bone 09/2013; · 4.46 Impact Factor
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    ABSTRACT: PURPOSE Predicting the biomechanical strength of the proximal femur is an important goal in the diagnosis of osteoporosis and in estimating fracture risk. This study uses statistical bone mineral density (BMD) features extracted from multi-detector computed tomography (MDCT) images of proximal femur specimens, and attempts to predict of bone strength through support vector regression (SVR). The performance of this system is compared with a standard approach that uses mean BMD and multi-regression models. METHOD AND MATERIALS Axial MDCT images were acquired from 146 proximal femur specimens using a 16-row scanner and a calibration phantom. Adaptive spherical volumes of interest (VOI) were positioned in the femoral head region for BMD value conversion and image analysis. VOIs of the BMD images were characterized through statistical moments as well as texture features extracted by the Gray Level Co-occurrence Matrix (GLCM) method. The specimens were then biomechanically tested, simulating a fall on the greater trochanter, and failure loads were recorded. All features were correlated to mechanical strength by multi-regression and SVR. The performance for different combination of feature groups was compared using root-mean-square error (RMSE) and Correlation (R2). A Wilcoxon signed-rank test was used to compare two RMSE distributions and test for statistical significant differences in performance. RESULTS The GLCM feature set using SVR showed the lowest prediction error (RMSE = 1.040 ± 0.143) and the highest correlation (R2 = 0.544), while the standard approach of using BMD and multi-regression had significantly larger error (RMSE = 1.093 ± 0.133) and a lower correlation (R2 = 0.490, p<0.0001) with mechanical strength. CONCLUSION These results indicate that the performance of BMD features extracted from MDCT images in predicting the biomechanical strength of proximal femur specimens can be significantly improved by using GLCM texture features in combination with advanced machine learning methods, such as support vector regression. CLINICAL RELEVANCE/APPLICATION Applying BMD characterization and machine learning techniques on MDCT image data in predicting trabecular bone strength can contribute to diagnosis and monitoring disease progression in osteoporosis.
    Radiological Society of North America 2012 Scientific Assembly and Annual Meeting; 11/2012
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    ABSTRACT: The purpose of this study was to investigate whether the combination of dual-energy X-ray absorptiometry (DXA)-based bone mass and magnetic resonance imaging (MRI)-based cortical and trabecular structural measures improves the prediction of radial bone strength. Thirty-eight left forearms were harvested from formalin-fixed human cadavers. Bone mineral content (BMC) and bone mineral density (BMD) of the distal radius were measured using DXA. Cortical and trabecular structural measures of the distal radius were computed in high-resolution 1.5T MR images. Cortical measures included average cortical thickness and cross-sectional area. Trabecular measures included morphometric and texture parameters. The forearms were biomechanically tested in a fall simulation to measure absolute radial bone strength (failure load). Relative radial bone strength was determined by dividing radial failure loads by age, body mass index, radius length, and average radius cross-sectional area, respectively. DXA derived BMC and BMD showed statistically significant (p < 0.05) correlations with absolute and relative radial bone strength (r ≤ 0.78). Correlation coefficients for cortical and trabecular structural measures with absolute and relative radial bone strength amounted up to r = 0.59 and r = 0.74, respectively, (p < 0.05). In combination with DXA-based bone mass, trabecular but not, cortical structural measures, added in multiple regression models significant (p < 0.05) information in predicting absolute and relative radial bone strength (up to R (adj) = 0.88). Thus, a combination of DXA-based bone mass and MRI-based trabecular structural measures most accurately predicted absolute and relative radial bone strength, whereas structural measures of the cortex did not provide significant additional information in combination with DXA.
    Journal of Bone and Mineral Metabolism 11/2012; · 2.22 Impact Factor
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    ABSTRACT: We analyze μ-CT tomographic images of human trabecular bone in vitro. We consider a sample consisting of 201 bone specimens harvested from six different skeletal sites with bone fraction in the range BV/TV ɛ [0.04, 0.075]. Using the local characterization of the bone trabecular network given by isotropic and anisotropic scaling indices, we apply classification algorithms in order to reveal structural similarities in the sample. The classification procedures based on isotropic and anisotropic scaling indices lead to different clustering solutions. This comparison helps revealing interesting site specific structural features connected to the intrinsic anisotropy of the trabecular network.
    Proc SPIE 02/2012;
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    ABSTRACT: Osteoporosis is a frequent skeletal disease characterised both by loss of bone mineral mass and deterioration of cancellous bone micro-architecture. It can be caused by mechanical disuse, estrogen deficiency or natural age-related resorption process. Numerical analysis of high-resolution images of the trabecular network is recognised as a powerful tool for assessment of structural characteristics. Using μCT images of 73 thoracic and 78 lumbar human vertebral specimens in vitro with isotropic resolution of 26μm we simulate bone atrophy as random resorption of bone surface voxels. Global morphological and topological characteristics provided by four Minkowski Functionals (MF) are calculated for two numerical resorption models with and without conservation of global topological connectivity of the trabecular network, which simulates different types of bone loss in osteoporosis, as it has been described in males and females. Diagnostic performance of morphological and topological characteristics as a function of relative bone loss is evaluated by a correlation analysis with respect to experimentally measured Maximum Compressive Strength (MCS). In both resorption models the second MF, which coincides with bone surface fraction BS/TV, demonstrates almost constant value of Pearson's correlation coefficient with respect to the relative bone loss ▵BV/TV. This morphological characteristic does not vary considerably under age-related random resorption and can be used for predicting bone strength in the elderly. The third and fourth MF demonstrate an increasing correlation coefficients with MCS after applying random bone surface thinning without preserving topological connectivity, what can be used for improvement of evaluation of the current state of the structure.
    Proc SPIE 02/2012;
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    ABSTRACT: According to Wolff's law bone remodels in response to the mechanical stresses it experiences so as to produce a minimal-weight structure that is adapted to its applied stresses. Consequently, the inner bone structure should show signs of adaptation to external forces acting on the bone. To test this paradigm, we investigate the relations between bone volume and structure for the trabecular bone using 3D μCT images taken from two different sites in the femur in vitro, namely from the femoral neck (88 specimens) and femoral trochanter (126 specimens). We determine the local structure of the trabecular network as well as its alignment with the direction of the external force acting on the bone by calculating isotropic (α) and anisotropic scaling indices (αz). Comparing global structure measures derived from the scaling indices (mean, variance) with the bone mass (BV/TV) we find that all correlations obey very accurately power laws with scaling exponents of 0.48 and 0.45 (<α>), -1.45 and -1.59 (var(μz)), 0.50 and 0.44 (<α>) and -1,47 and -1.32 (var(μz)) (neck and trochanter respectively). Thus, the relations for the isotropic scaling indices turn out to be siteindependent, albeit the mechanical stress to which the femoral neck is exposed is much larger than that for the trochanter. We find, however, differences in the degree of alignment of the trabeculae as reflected by the moments of the distribution of the anisotropic scaling indices. In summary, the mass-structure scaling relations of the bone probes taken from the two different sites of the femur show surprisingly small variations. Thus, a naïve interpretation of Wolff's law may not universally valid.
    Proc SPIE 02/2012;
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    ABSTRACT: High-resolution peripheral quantitative computed tomography (HR-pQCT) is clinically available today and provides a non-invasive measure of 3D bone geometry and micro-architecture with unprecedented detail. In combination with microarchitectural finite element (μFE) models it can be used to determine bone strength using a strain-based failure criterion. Yet, images from only a relatively small part of the radius are acquired and it is not known whether the region recommended for clinical measurements does predict forearm fracture load best. Furthermore, it is questionable whether the currently used failure criterion is optimal because of improvements in image resolution, changes in the clinically measured volume of interest, and because the failure criterion depends on the amount of bone present. Hence, we hypothesized that bone strength estimates would improve by measuring a region closer to the subchondral plate, and by defining a failure criterion that would be independent of the measured volume of interest. To answer our hypotheses, 20% of the distal forearm length from 100 cadaveric but intact human forearms was measured using HR-pQCT. μFE bone strength was analyzed for different subvolumes, as well as for the entire 20% of the distal radius length. Specifically, failure criteria were developed that provided accurate estimates of bone strength as assessed experimentally. It was shown that distal volumes were better in predicting bone strength than more proximal ones. Clinically speaking, this would argue to move the volume of interest for the HR-pQCT measurements even more distally than currently recommended by the manufacturer. Furthermore, new parameter settings using the strain-based failure criterion are presented providing better accuracy for bone strength estimates.
    Bone 03/2011; 48(6):1232-8. · 4.46 Impact Factor
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    ABSTRACT: We analyse mu-CT tomographic images of human trabecular bone in vitro. We consider a sample consisting of 201 bone specimens harvested from six different skeletal sites within a narrow range of bone fraction values. Using the characterization of the trabecular bone network given by local Minkowski Functionals, we apply classification algorithms in order to reveal structural similarities in the sample. Clusters show some interesting specific structural features, like compact, porous, and fragmented structures. The contribution of the different skeletal sites to these clusters indicate some variability due to intrinsic structural differences of the specific skeletal site.
    Proc SPIE 03/2011;
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    ABSTRACT: The objective of this experimental finite element (FE) study is to test the hypothesis that strain distributions coincide with the occurrence of cervical versus trochanteric hip fractures during loading conditions simulating a sideways fall, and that the cervical versus trochanteric principal strain ratio predicts different fracture patterns. Cadaver femora (female, 83 +/- 9 years) were CT scanned and mechanically tested simulating a fall. Thirteen cervical and 13 trochanteric fracture cases were selected for FE analysis. Principal strain distributions were analysed, and strain ratio epsilon(C)/epsilon(T) for strain patterns over the cervical and trochanteric regions was computed. The ratio epsilon(C)/epsilon(T) in the femora with cervical fractures (mean +/- SD 1.103 +/- 0.127) differed from that in trochanteric fractures (0.925 +/- 0.137) (p = 0.001). The significant difference in the strain ratio between fracture types remained after accounting for femoral neck and trochanteric BMD (p = 0.014), showing that it is independent of BMD. Area under the ROC curve was 0.858 in the discrimination of fracture types. The model predicted the experimental fracture type correctly in 22 of 26 cases. The cervical versus trochanteric region principal strain ratio differed significantly between femora with experimental cervical versus trochanteric fractures, and 85% agreement was achieved for the occurrence of hip fracture types using a simple FE model.
    Medical & Biological Engineering 07/2010; 48(7):711-7. · 1.76 Impact Factor
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    ABSTRACT: According to Wolff's law bone remodels in response to the mechanical stresses it experiences so as to produce a minimal-weight structure that is adapted to its applied stresses. Here, we investigate the relations between bone volume and structure for the trabecular bone using 3D muCT images taken from different skeletal sites in vitro, namely from the distal radii (96 specimens), thoracic (73 specimens) and lumbar vertebrae (78 specimens). We determine the local structure of the trabecular network by calculating isotropic and anisotropic scaling indices (alpha, alphaz). These measures have been proven to be able to discriminate rod- from sheet-like structures and to quantify the alignment of structures with respect to a preferential direction as given by the direction of the external force. Comparing global structure measures derived from the scaling indices (mean, standard deviation) with the bone mass (BV/TV) we find that all correlations obey very accurately power laws with scaling exponents of 0.14, 0.12, 0.15 (~), -0.2, -017, -0.17 (sigma(alphaz)), 0.09, 0.05, 0.07 (~) and -0.20, -0.11 ,-0.13 (sigma(alphaz)) distal radius, thoracic vertebra and lumbar vertebra respectively. Thus, these relations turn out to be site-independent, albeit the mechanical stresses to which the bones of the forearm and the spine are exposed, are quite different. The similar alignment might not be in agreement with a universal validity of Wolff's law. On the other hand, such universal power law relations may allow to develop additional diagnostic means to better assess healthy and osteoporotic bone.
    Proc SPIE 03/2010;
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    ABSTRACT: Spine fractures are the most frequent complication of osteoporosis, a disease characterized by low bone mass and structural deterioration of bone tissue. In case of the spine, the trabecular network plays the main role in load carrying and distribution. A correct description of mechanical properties of this bone structure helps to differentiate between strong and weak bones and can be useful for fracture prediction and treatment monitoring. By means of the finite element method (FEM), applied to muCT images, we modelled biomechanical processes in probes during loading and correlated the estimated failure load with the maximum compressive strength (MCS), obtained in real biomechanical tests. We studied a sample of 151 specimens taken from the trabecular part of human vertebrae in vitro, visualised using muCT imaging at an isotropic resolution of 26mum and tested by uniaxial compression. Besides the standard way of estimating failure load, which takes into account only strong micro-fractures, we also included small micro-fractures, what improved the correlation with MCS (Pearson's correlation coefficient r=0.78 vs. r=0.58). This correlation coefficient was larger than that for both the standard morphometric parameters (r=0.73 for bone volume fraction) and for texture measures defined by the local (an-) isotropic scaling indices method (r=0.55) and Minkowski Functionals (r=0.61). However, the performance of the FEM was different for subsamples selected according to the MCS value. The correlation increased for strong specimens (r=0.88), slightly decreased for weak specimens (r=0.68) and markedly dropped for specimens with medium MCS, e.g. between 60
    Proc SPIE 02/2009;
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    ABSTRACT: Osteoporosis is bone disease which leads to low bone mass and the deterioration of the bone micro-architecture. Rarefied bone structures are more susceptible to fractures which are the worst complications of osteoporosis. Bone mineral density is considered to be the standard technique for predicting the bone strength and the effects of drug therapy. However, other properties of the bone like the trabecular structure and connectivity may also contribute. Here, we analyze mu-CT tomographic images for a sample of 151 specimens taken from human vertebrae in vitro. Using the local structural characterization of the bone trabecular network given by isotropic and anisotropic scaling indices, we generate structural decompositions of the mu-CT image and quantify the resulting patterns applying topological measures, namely the Minkowski Functionals (MF). The values of the MF are then used to assess the biomechanical properties of trabecular bone via a correlation analysis. Biomechanical properties were quantified by the maximum compressive strength calculated in an uniaxial compression test. We compare our results with those obtained using standard global histomorphometric parameters and the bone fraction BV/TV . Results obtained using structural decompositions obtained from anisotropic scaling indices were superior to those given by isotropic scaling indices. The highest correlation coefficient (r = 0.72) was better than those obtained for the standard global histomorphometric parameters and only comparable with the one given by BV/TV. Our results suggest that plate-like and dense column-like structures aligned along the direction of the external force play a relevant role for the prediction of bone strength.
    Proc SPIE 02/2009;
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    ABSTRACT: To investigate differences in magnetic resonance imaging (MRI) of trabecular bone at 1.5T and 3.0T and to specifically study noise effects on the visualization and quantification of trabecular architecture using conventional histomorphometric and nonlinear measures of bone structure. Sagittal MR images of 43 calcaneus specimens (donor age: 81 +/- 10 years) were acquired at 1.5T and 3.0T using gradient echo sequences. Noise was added to obtain six sets of images with decreasing signal-to-noise ratios (SNRs). Micro-CT images were obtained from biopsies taken from 37 calcaneus samples and bone strength was determined. Morphometric and nonlinear structure parameters were calculated in all datasets. Originally, SNR was 1.5 times higher at 3.0T. In the simulated image sets, SNR was similar at both fields. Trabecular dimensions measured by microCT were adequately estimated by MRI, with residual errors (e(r)), ranging from 16% to 2.7% at 3.0T. Comparing e(r) at similar SNR, 3.0T consistently displayed lower errors than 1.5T (eg, bone fraction at SNR approximately 4: e(r)[3.0T] = 15%; e(r)[1.5T] = 21%, P < 0.05). The advances of 3.0T compared to 1.5T in visualizing trabecular bone structure are partially SNR-independent. The better performance at 3.0T may be explained by pronounced susceptibility, enhancing the visualization of thin trabecular structures.
    Journal of Magnetic Resonance Imaging 12/2008; 29(1):132-40. · 2.57 Impact Factor
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    ABSTRACT: We tested the hypothesis that the age dependence of trabecular bone microstructure differs between men and women and is specific to skeletal site. Furthermore, we aimed to investigate the microstructural pattern of bone loss in aging. Microstructural properties of trabecular bone were measured in vitro in 75 men and 75 age-matched women (age, 52-99 yr) using microCT. Trabecular bone samples were scanned at a 26-microm isotropic resolution at seven anatomical sites (i.e., distal radius, T(10) and L(2) vertebrae, iliac crest, femoral neck and trochanter, and calcaneus). DXA measurements were obtained at the distal radius and proximal femur and QCT was used at T(12). No significant decrease in bone density or structure with age was found in men using microCT, DXA, or QCT at any of the anatomical sites. In women, a significant age-dependent decrease in BV/TV was observed at most sites, which was strongest at the iliac crest and weakest at the distal radius. At most sites, the reduction in BV/TV was associated with an increase in structure model index, decrease in Tb.N, and an increase in Tb.Sp. Only in the calcaneus was it associated with a significant decrease in Tb.Th. In conclusion, a significant, site-specific correlation of trabecular bone microstructure with age was found in women but not in men of advanced age. The microstructural basis by which a loss of BV/TV occurs with age can vary between anatomical sites.
    Journal of bone and mineral research: the official journal of the American Society for Bone and Mineral Research 08/2008; 23(12):1964-73. · 6.04 Impact Factor
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    ABSTRACT: Osteoporosis leads to an increased risk of bone fracture. While bone density and architecture can be assessed in vivo with increasing accuracy using CT and MRI, their relationship with the critical mechanical properties at various anatomical sites remain unclear. The objective of this study was to quantify the quasi-static compressive mechanical properties of human trabecular bone among different skeletal sites and compare their relationships with bone volume fraction and a measure of microstructural anisotropy called fabric. Over 600 trabecular bone samples from six skeletal sites were assessed by microCT and tested in uniaxial compression. Bone volume fraction correlated positively with elastic modulus, yield stress, ultimate stress, and the relationships depended strongly on skeletal site. The account of fabric improved these correlations substantially, especially when the data of all sites were pooled together, but the fabric-mechanical property relationships remained somewhat distinct among the anatomical sites. The study confirms that, beyond volume fraction, fabric plays an important role in determining the mechanical properties of trabecular bone and should be exploited in mechanical analysis of clinically relevant sites of the human skeleton.
    Biomechanics and Modeling in Mechanobiology 03/2008; 7(1):27-42. · 3.33 Impact Factor
  • Journal of Biomechanics - J BIOMECH. 01/2008; 41.
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    ABSTRACT: Newly developed fuzzy logic-derived structural parameters were used to characterize trabecular bone architecture in high-resolution magnetic resonance imaging (HR-MRI) of human cadaver calcaneus specimens. These parameters were compared to standard histomorphological structural measures and analyzed concerning performance in discriminating vertebral fracture status and estimating proximal femur fracture load. Sets of 60 sagittal 1.5 T and 3.0 T HR-MRI images of the calcaneus were obtained in 39 cadavers using a fast gradient recalled echo sequence. Structural parameters equivalent to bone histomorphometry and fuzzy logic-derived parameters were calculated using two chosen regions of interest. Calcaneal, spine, and hip bone mineral density (BMD) measurements were also obtained. Fracture status of the thoracic and lumbar spine was assessed on lateral radiographs. Finally, mechanical strength testing of the proximal femur was performed. Diagnostic performance in discriminating vertebral fracture status and estimating femoral fracture load was calculated using regression analyses, two-tailed t-tests of significance, and receiver operating characteristic (ROC) analyses. Significant correlations were obtained at both field strengths between all structural and fuzzy logic parameters (r up to 0.92). Correlations between histomorphological or fuzzy logic parameters and calcaneal BMD were mostly significant (r up to 0.78). ROC analyses demonstrated that standard structural parameters were able to differentiate persons with and without vertebral fractures (area under the curve [A(Z)] up to 0.73). However, none of the parameters obtained in the 1.5-T images and none of the fuzzy logic parameters discriminated persons with and without vertebral fractures. Significant correlations were found between fuzzy or structural parameters and femoral fracture load. Using multiple regression analysis, none of the structural or fuzzy parameters were found to add discriminative value to BMD alone. In summary significant correlations were obtained at both field strengths between all structural and fuzzy logic parameters. However, fuzzy logic-based calcaneal parameters were not well suited for vertebral fracture discrimination. Although significant correlations were found between fuzzy or structural parameters and femoral fracture load, multiple regression analysis showed limited improvement for estimating femoral failure load in addition to femoral BMD alone. Local femoral measurements are still needed to estimate femoral bone strength. Overall, parameters obtained at 3.0 T performed better than those at 1.5 T.
    Calcified Tissue International 11/2007; 81(4):294-304. · 2.75 Impact Factor

Publication Stats

629 Citations
98.04 Total Impact Points

Institutions

  • 2004–2014
    • Paracelsus Medical University Salzburg
      • Institute of Anatomy und Musculoskeletal Research
      Salzburg, Salzburg, Austria
  • 2006–2010
    • University of Oulu
      • Department of Medical Technology
      Oulu, Oulu, Finland
  • 2004–2008
    • Technische Universität München
      • Institut für Radiologie
      München, Bavaria, Germany
  • 2007
    • University of California, San Francisco
      • Department of Radiology and Biomedical Imaging
      San Francisco, CA, United States
  • 2002–2007
    • Ludwig-Maximilian-University of Munich
      • Institute of Veterinary Anatomy
      München, Bavaria, Germany