QCT Images Research Articles

Explainable machine-learning-based prediction of QCT/FEA-calculated femoral strength under stance loading configuration using radiomics features.

Finite element analysis can provide precise femoral strength assessment. However, its modeling procedures were complex and time-consuming. This study aimed to develop a model to evaluate femoral strength calculated by quantitative computed tomography-based finite element analysis (QCT/FEA) under stance loading configuration, offering an effective, simple, and explainable method. One hundred participants with hip QCT images were selected from the Hong Kong part of the Osteoporotic fractures in men cohort. Radiomics features were extracted from QCT images. Filter method, Pearson correlation analysis, and least absolute shrinkage and selection operatormethod were employed for feature selection and dimension reduction. The remainingfeatures were utilized as inputs, and femoral strengths were calculated as the ground truth through QCT/FEA. Support vector regressionwas applied to develop a femoral strength prediction model. The influence of various numbers of input features on prediction performance was compared, and the femoral strength prediction model was established. Finally, Shapley additive explanation, accumulated local effects, and partial dependency plotmethods were used to explain the model. The results indicated that the model performed best when six radiomics features were selected. The coefficient of determination (R2), the root mean square error, the normalized root mean square error, and the mean squared erroron the testing set were 0.820, 1016.299 N, 10.645%, and 750.827 N, respectively. Additionally, these features all positively contributed to femoral strength prediction. In conclusion, this study provided a noninvasive, effective, and explainable method of femoral strength assessment, and it may have clinical application potential.

Validation of bone mineral density measurement using quantitative CBCT image based on deep learning

The bone mineral density (BMD) measurement is a direct method of estimating human bone mass for diagnosing osteoporosis, and performed to objectively evaluate bone quality before implant surgery in dental clinics. The objective of this study was to validate the accuracy and reliability of BMD measurements made using quantitative cone-beam CT (CBCT) image based on deep learning by applying the method to clinical data from actual patients. Datasets containing 7500 pairs of CT and CBCT axial slice images from 30 patients were used to train a previously developed deep-learning model (QCBCT-NET). We selected 36 volumes of interest in the CBCT images for each patient in the bone regions of potential implants sites on the maxilla and mandible. We compared the BMDs shown in the quantitative CBCT (QCBCT) images with those in the conventional CBCT (CAL_CBCT) images at the various bone sites of interest across the entire field of view (FOV) using the performance metrics of the MAE, RMSE, MAPE (mean absolute percentage error), R2 (coefficient of determination), and SEE (standard error of estimation). Compared with the ground truth (QCT) images, the accuracy of the BMD measurements from the QCBCT images showed an RMSE of 83.41 mg/cm3, MAE of 67.94 mg/cm3, and MAPE of 8.32% across all the bone sites of interest, whereas for the CAL_CBCT images, those values were 491.15 mg/cm3, 460.52 mg/cm3, and 54.29%, respectively. The linear regression between the QCBCT and QCT images showed a slope of 1.00 and a R2 of 0.85, whereas for the CAL_CBCT images, those values were 0.32 and 0.24, respectively. The overall SEE between the QCBCT images and QCT images was 81.06 mg/cm3, whereas the SEE for the CAL_CBCT images was 109.32 mg/cm3. The QCBCT images thus showed better accuracy, linearity, and uniformity than the CAL_CBCT images across the entire FOV. The BMD measurements from the quantitative CBCT images showed high accuracy, linearity, and uniformity regardless of the relative geometric positions of the bone in the potential implant site. When applied to actual patient CBCT images, the CBCT-based quantitative BMD measurement based on deep learning demonstrated high accuracy and reliability across the entire FOV.

Alternatives to DEXA for the assessment of bone density: a systematic review of the literature and future recommendations.

Osteoporosis has significant implications in spine fusion surgery, for which reduced spinal bone mineral density (BMD) can result in complications and poorer outcomes. Currently, dual-energy x-ray absorptiometry (DEXA) is the gold standard for radiographic diagnosis of osteoporosis, although DEXA accuracy may be limited by the presence of degenerative spinal pathology. In recent years, there has been an evolving interest in using alternative imaging, including CT and MRI, to assess BMD. In this systematic review of the literature, the authors assessed the use and effectiveness of MRI, opportunistic CT (oCT), and quantitative CT (qCT) to measure BMD. In accordance with the PRISMA guidelines, the authors conducted a systematic search for articles posted on PubMed between the years 2000 and 2022 by using the keywords "opportunistic CT, quantitative CT, MRI" AND "bone density" AND "spine." Inclusion criteria consisted of articles written in English that reported studies pertaining to human or cadaveric subjects, and studies including a measure of spinal BMD. Articles not related to spinal BMD, osteoporosis, or spinal surgery or reports of studies that did not include the use of spinal MRI or CT were excluded. Key study outcomes were extracted from included articles, and qualitative analysis was subsequently performed. The literature search yielded 302 articles. Forty-two articles reported studies that met the final inclusion criteria. Eighteen studies utilized MRI protocols to correlate spinal BMD with vertebral bone quality scores, M-scores, and quantitative perfusion markers. Eight studies correlated oCT with spinal BMD, and 16 studies correlated qCT with spinal BMD. With oCT and qCT imaging, there was consensus that Hounsfield unit (HU) values > 160 demonstrated significant reduction in risk of osteoporosis, whereas HU values < 110 were significantly correlated with osteoporosis. Osteoporosis is increasingly recognized as a significant risk factor for complications after spinal fusion surgery. Consequently, preoperative assessment of BMD is a critical factor to consider in planning surgical treatment. Although DEXA has been the gold standard for BMD measurement, other imaging modalities, including MRI, oCT, and qCT, appear to be viable alternatives and may offer cost and time savings.

2D-3D reconstruction of the proximal femur from DXA scans: Evaluation of the 3D-Shaper software.

Introduction: Osteoporosis is currently diagnosed based on areal bone mineral density (aBMD) computed from 2D DXA scans. However, aBMD is a limited surrogate for femoral strength since it does not account for 3D bone geometry and density distribution. QCT scans combined with finite element (FE) analysis can deliver improved femoral strength predictions. However, non-negligible radiation dose and high costs prevent a systematic usage of this technique for screening purposes. As an alternative, the 3D-Shaper software (3D-Shaper Medical, Spain) reconstructs the 3D shape and density distribution of the femur from 2D DXA scans. This approach could deliver a more accurate estimation of femoral strength than aBMD by using FE analysis on the reconstructed 3D DXA. Methods: Here we present the first independent evaluation of the software, using a dataset of 77 ex vivo femora. We extend a prior evaluation by including the density distribution differences, the spatial correlation of density values and an FE analysis. Yet, cortical thickness is left out of this evaluation, since the cortex is not resolved in our FE models. Results: We found an average surface distance of 1.16mm between 3D DXA and QCT images, which shows a good reconstruction of the bone geometry. Although BMD values obtained from 3D DXA and QCT correlated well (r 2 = 0.92), the 3D DXA BMD were systematically lower. The average BMD difference amounted to 64mg/cm3, more than one-third of the 3D DXA BMD. Furthermore, the low correlation (r 2 = 0.48) between density values of both images indicates a limited reconstruction of the 3D density distribution. FE results were in good agreement between QCT and 3D DXA images, with a high coefficient of determination (r 2 = 0.88). However, this correlation was not statistically different from a direct prediction by aBMD. Moreover, we found differences in the fracture patterns between the two image types. QCT-based FE analysis resulted mostly in femoral neck fractures and 3D DXA-based FE in subcapital or pertrochanteric fractures. Discussion: In conclusion, 3D-Shaper generates an altered BMD distribution compared to QCT but, after careful density calibration, shows an interesting potential for deriving a standardized femoral strength from a DXA scan.

Effects of four-year cyclic versus two-year daily teriparatide treatment on volumetric bone density and bone strength in postmenopausal women with osteoporosis.

To evaluate the effects of cyclic vs daily teriparatide treatment (TPTD) on volumetric bone mineral density (vBMD) and bone strength at the hip and spine in women who were previously untreated. A total of 86 women were randomized to a 24-month open label treatment of either daily TPTD (20μg daily) or cyclic TPTD (20μg daily for 3months followed by 3months off). During a 2-year extension, women in the daily TPTD group were switched to alendronate (ALN) and those in the cyclic TPTD group continued on cyclic TPTD (without any ALN). QCT images were acquired at baseline, 2-years (n=54) and 4-years (n=35) and analyzed for volumetric integral, cortical and trabecular bone mineral density (vBMD) and bone strength (by finite element analysis) at the hip and spine. The primary analysis presented here compared the responses across equal total TPTD doses (2years daily vs 4years cyclic). In the spine, integral vBMD and strength increased substantially after 2years daily and 4years cyclic TPTD, with no significant differences (vBMD +12% vs +11%, respectively, p=0.70; spine strength +21% vs +16%, respectively, p=0.35). At the hip, the gains were smaller, but again no significant differences were detected between the groups for the increases in either vBMD (+2% in both groups, p=0.97) or hip strength (3% vs 3%, p=0.91). In the spine, the vBMD increment was about twice as large in the trabecular vs peripheral compartment; in the hip, significant vBMD gain was seen only in the trabecular compartment. The gains in volumetric BMD and bone strength for an equivalent dose of TPTD did not depend on whether it was administered every day over two years or cyclically over four years.

Quantitative CT Characteristics of Cluster Phenotypes in the Severe Asthma Research Program Cohorts.

Background Clustering key clinical characteristics of participants in the Severe Asthma Research Program (SARP), a large, multicenter prospective observational study of patients with asthma and healthy controls, has led to the identification of novel asthma phenotypes. Purpose To determine whether quantitative CT (qCT) could help distinguish between clinical asthma phenotypes. Materials and Methods A retrospective cross-sectional analysis was conducted with the use of qCT images (maximal bronchodilation at total lung capacity [TLC], or inspiration, and functional residual capacity [FRC], or expiration) from the cluster phenotypes of SARP participants (cluster 1: minimal disease; cluster 2: mild, reversible; cluster 3: obese asthma; cluster 4: severe, reversible; cluster 5: severe, irreversible) enrolled between September 2001 and December 2015. Airway morphometry was performed along standard paths (RB1, RB4, RB10, LB1, and LB10). Corresponding voxels from TLC and FRC images were mapped with use of deformable image registration to characterize disease probability maps (DPMs) of functional small airway disease (fSAD), voxel-level volume changes (Jacobian), and isotropy (anisotropic deformation index [ADI]). The association between cluster assignment and qCT measures was evaluated using linear mixed models. Results A total of 455 participants were evaluated with cluster assignments and CT (mean age ± SD, 42.1 years ± 14.7; 270 women). Airway morphometry had limited ability to help discern between clusters. DPM fSAD was highest in cluster 5 (cluster 1 in SARP III: 19.0% ± 20.6; cluster 2: 18.9% ± 13.3; cluster 3: 24.9% ± 13.1; cluster 4: 24.1% ± 8.4; cluster 5: 38.8% ± 14.4; P < .001). Lower whole-lung Jacobian and ADI values were associated with greater cluster severity. Compared to cluster 1, cluster 5 lung expansion was 31% smaller (Jacobian in SARP III cohort: 2.31 ± 0.6 vs 1.61 ± 0.3, respectively, P < .001) and 34% more isotropic (ADI in SARP III cohort: 0.40 ± 0.1 vs 0.61 ± 0.2, P < .001). Within-lung Jacobian and ADI SDs decreased as severity worsened (Jacobian SD in SARP III cohort: 0.90 ± 0.4 for cluster 1; 0.79 ± 0.3 for cluster 2; 0.62 ± 0.2 for cluster 3; 0.63 ± 0.2 for cluster 4; and 0.41 ± 0.2 for cluster 5; P < .001). Conclusion Quantitative CT assessments of the degree and intraindividual regional variability of lung expansion distinguished between well-established clinical phenotypes among participants with asthma from the Severe Asthma Research Program study. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Verschakelen in this issue.

Computed Tomography Features of Lung Structure Have Utility for Differentiating Malignant and Benign Pulmonary Nodules.

Chronic obstructive pulmonary disease (COPD) is a known comorbidity for lung cancer independent of smoking history. Quantitative computed tomography (qCT) imaging features related to COPD have shown promise in the assessment of lung cancer risk. We hypothesize that qCT features from the lung, lobe, and airway tree related to the location of the pulmonary nodule can be used to provide informative malignancy risk assessment. A total of 183 qCT features were extracted from 278 individuals with a solitary pulmonary nodule of known diagnosis (71 malignant, 207 benign). These included histogram and airway characteristics of the lungs, lobe, and segmental paths. Performances of the least absolute shrinkage and selection operator (LASSO) regression analysis and an ensemble of neural networks (ENN) were compared for feature set selection and classification on a testing cohort of 49 additional individuals (15 malignant, 34 benign). The LASSO and ENN methods produced different feature sets for classification with LASSO selecting fewer qCT features (7) than the ENN (17). The LASSO model with the highest performing training area under the curve (AUC) (0.80) incorporated automatically extracted features and reader-measured nodule diameter with a testing AUC of 0.62. The ENN model with the highest performing AUC (0.77) also incorporated qCT and reader diameter but maintained higher testing performance AUC (0.79). Automatically extracted qCT imaging features of the lung can be informative of the differentiation between individuals with malignant pulmonary nodules and those with benign pulmonary nodules, without requiring nodule segmentation and analysis.

Menstrual cycle impacts lung structure measures derived from quantitative computed tomography.

Quantitative computed tomography (qCT) is being increasingly incorporated in research studies and clinical trials aimed at understanding lung disease risk, progression, exacerbations, and intervention response. Menstrual cycle-based changes in lung function are recognized; however, the impact on qCT measures is currently unknown. We hypothesize that the menstrual cycle impacts qCT-derived measures of lung structure in healthy women and that the degree of measurement change may be mitigated in subjects on cyclic hormonal birth control. Thirty-one non-smoking, healthy women with regular menstrual cycles (16 of which were on cyclic hormonal birth control) underwent pulmonary function testing and qCT imaging at both menses and early luteal phase time points. Data were evaluated to identify lung measurements which changed significantly across the two key time points and to compare degree of change across metrics for the sub-cohort with versus without birth control. The segmental airway measurements were larger and mean lung density was higher at menses compared to the early luteal phase. The sub-cohort with cyclic hormonal birth control did not have less evidence of measurement difference over the menstrual cycle compared to the sub-cohort without hormonal birth control. This study provides evidence that qCT-derived measures from the lung are impacted by the female menstrual cycle. This indicates studies seeking to use qCT as a more sensitive measure of cross-sectional differences or longitudinal changes in these derived lung measurements should consider acquiring data at a consistent time in the menstrual cycle for pre-menopausal women and warrants further exploration. • Lung measurements from chest computed tomography are used in multicenter studies exploring lung disease progression and treatment response. • The menstrual cycle impacts lung structure measurements. • Cyclic variability should be considered when evaluating longitudinal change with CT in menstruating women.

Androgen Deprivation Therapy Differentially Impacts Bone and Muscle in the Short Term in Physically Active Men With Prostate Cancer.

ABSTRACTAndrogen deprivation therapy (ADT) is a cornerstone of advanced prostate cancer (PCa) therapy. Its use is associated with a loss of bone mineral density (BMD) and a greater risk of falls and osteoporotic fractures. In this prospective cohort study, we examined the impact of ADT on muscle and bone strength in men initiating ADT for PCa. Participants were evaluated at three time points: immediately before (week 0), and 6 and 24 weeks after ADT initiation. Study measures included fasting blood levels (for markers of muscle and bone metabolic activity), MRI and QCT imaging (for muscle fat content, and bone density and architecture), and validated clinical tests of muscle strength and gait. Sixteen men completed all study visits. At baseline and throughout the study, participants exercised a median of four times/week, but still experienced weight gain (+2.0 kg at week 24 versus week 0, p = 0.004). Biochemically, all men sustained dramatic early and persistent reductions in sex hormones post‐ADT, along with a progressive and significant increase in serum C‐telopeptide of type I collagen (CTX, +84% at week 24 versus week 0). There was a trend for rise in serum sclerostin (p = 0.09) and interleukin 6 (IL‐6) (p = 0.08), but no significant change in serum myostatin (p = 0.99). Volumetric BMD by QCT declined significantly at the femoral neck (−3.7% at week 24 versus week 0), particularly at the trabecular compartment. On MRI, there were no significant changes in thigh muscle fat fraction. On physical testing, men developed weaker grip strength, but experienced no worsening in lower extremity and lumbar spine muscle strength, or on functional tests of gait. In conclusion, in physically active men, ADT for 24 weeks results in a significant increase in bone resorption and reduction in BMD, but nonsignificant changes in thigh muscle quality (on imaging) or strength and gait (on functional testing). © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Quantitative CT image-based structural and functional changes during asthma acute exacerbations.

Asthma acute exacerbations (AE) have been investigated using quantitative computed tomography (QCT)-based imaging metrics, but QCT has not yet been used to investigate a comprehensive set of imaging metrics during AE. This study aims to explore imaging features, captured both at segmental and parenchymal scales, during asthma AE compared with those in stable asthma (SA). Two sets of the QCT images at total lung capacity (TLC) and functional residual capacity (FRC) were captured for 14 subjects during asthma AE and in SA phase, respectively. We calculated airway wall thickness (WT), hydraulic diameter (Dh), and airway circularity (Cr) of the 36 segmental airways; percentage of functional small airway disease (fSAD%); percentage of emphysema; tissue fraction (βtiss); and coefficient of variation of βtiss (CV of βtiss). We performed Spearman correlation tests for changes in QCT metrics and pulmonary function tests, measured in AE and SA. During asthma AE, structural metrics, that is, WT, Dh, and Cr, were not changed significantly. In functional metrics, CV of βtiss at FRC indicating the heterogeneity of lung tissue distribution was significantly increased, whereas the mean of βtiss at FRC did not change during AE. An increase of fSAD% during AE was most correlated with a decrease of forced expiratory volume in 1 s and forced vital capacity, especially in the lower lobes. This study demonstrates that the heterogeneous feature of βtiss measured at lower lobes is more noticeable during asthma AE, compared with other traditional imaging metrics. This metric could be utilized to identify unique features during asthma AE.NEW & NOTEWORTHY Using two sets of inspiration and expiration images, the difference of segmental airway structure and parenchymal lung function is assessed by comparing the QCT images during asthma acute exacerbations with those in stable asthma. This study also introduces a useful application of an imaging-based metric, estimating the heterogeneity of tissue distribution. This could be a phenotype for the asthma acute exacerbation.

Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia.

Increasingly, quantitative lung computed tomography (qCT)-derived metrics are providing novel insights into chronic inflammatory lung diseases, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and more. Metrics related to parenchymal, airway, and vascular anatomy together with various measures associated with lung function including regional parenchymal mechanics, air trapping associated with functional small airways disease, and dual-energy derived measures of perfused blood volume are offering the ability to characterize disease phenotypes associated with the chronic inflammatory pulmonary diseases. With the emergence of COVID-19, together with its widely varying degrees of severity, its rapid progression in some cases, and the potential for lengthy post-COVID-19 morbidity, there is a new role in applying well-established qCT-based metrics. Based on the utility of qCT tools in other lung diseases, previously validated supervised classical machine learning methods, and emerging unsupervised machine learning and deep-learning approaches, we are now able to provide desperately needed insight into the acute and the chronic phases of this inflammatory lung disease. The potential areas in which qCT imaging can be beneficial include improved accuracy of diagnosis, identification of clinically distinct phenotypes, improvement of disease prognosis, stratification of care, and early objective evaluation of intervention response. There is also a potential role for qCT in evaluating an increasing population of post-COVID-19 lung parenchymal changes such as fibrosis. In this work, we discuss the basis of various lung qCT methods, using case-examples to highlight their potential application as a tool for the exploration and characterization of COVID-19, and offer scanning protocols to serve as templates for imaging the lung such that these established qCT analyses have the best chance at yielding the much needed new insights.

Prediction of lumbar vertebral strength of elderly men based on quantitative computed tomography images using machine learning

Vertebral fracture is common in elderly populations. The main factor contributing to vertebral fracture is the reduced vertebral strength. This study aimed to predict vertebral strength based on clinical QCT images by using machine learning. Eighty subjects with QCT data of lumbar spine were randomly selected from the MrOS cohorts. L1 vertebral strengths were computed by QCT-based finite element analysis. A total of 58 features of each L1 vertebral body were extracted from QCT images, including grayscale distribution, grayscale values of 39 partitioned regions, BMDQCT, structural rigidity, axial rigidity, and BMDQCTAmin. Feature selection and dimensionality reduction were used to simplify the 58 features. General regression neural network and support vector regression models were developed to predict vertebral strength. Performance of prediction models was quantified by the mean squared error, the coefficient of determination, the mean bias, and the SD of bias. The 58 parameters were simplified to five features (grayscale value of the 60% percentile, grayscale values of three specific partitioned regions, and BMDQCTAmin) and nine principal components (PCs). High accuracy was achieved by using the five features or the nine PCs to predict vertebral strength. This study provided an effective approach to predict vertebral strength and showed that it may have great potential in clinical applications for noninvasive assessment of vertebral fracture risk.

Supervised learning for bone shape and cortical thickness estimation from CT images for finite element analysis.

Knowledge about the thickness of the cortical bone is of high interest for fracture risk assessment. Most finite element model solutions overlook this information because of the coarse resolution of the CT images. To circumvent this limitation, a three-steps approach is proposed. 1) Two initial surface meshes approximating the outer and inner cortical surfaces are generated via a shape regression based on morphometric features and statistical shape model parameters. 2) The meshes are then corrected locally using a supervised learning model build from image features extracted from pairs of QCT (0.3-1 mm resolution) and HRpQCT images (82 µm resolution). As the resulting meshes better follow the cortical surfaces, the cortical thickness can be estimated at sub-voxel precision. 3) The meshes are finally regularized by a Gaussian process model featuring a two-kernel model, which seamlessly enables smoothness and shape-awareness priors during regularization. The resulting meshes yield high-quality mesh element properties, suitable for construction of tetrahedral meshes and finite element simulations. This pipeline was applied to 36 pairs of proximal femurs (17 males, 19 females, 76 ± 12 years) scanned under QCT and HRpQCT modalities. On a set of leave-one-out experiments, we quantified accuracy (root mean square error = 0.36 ± 0.29 mm) and robustness (Hausdorff distance = 3.90 ± 1.57 mm) of the outer surface meshes. The error in the estimated cortical thickness (0.05 ± 0.40 mm), and the tetrahedral mesh quality (aspect ratio = 1.4 ± 0.02) are also reported. The proposed pipeline produces finite element meshes with patient-specific bone shape and sub-voxel cortical thickness directly from CT scans. It also ensures that the nodes and elements numbering remains consistent and independent of the morphology, which is a distinct advantage in population studies.

Correspondence between bone mineral density and intervertebral disc degeneration across age and sex.

While the decline of bone mineral density (BMD) in the aging spine is well established, the extent to which age influences BMD distribution within the vertebra is less clear. Measures of regional BMD (rBMD) may improve predictions of vertebral strength and suggest how vertebrae might adapt with intervertebral disc degeneration. Thus, we aimed to assess how rBMD values were associated with age, sex, and disc height loss (DHL). We measured rBMD in the L3 vertebra of 377 participants from the Framingham Heart Study (41-83years, 181 M/196 F). Integral (Int.BMD) and trabecular BMD (Tb.BMD) were measured from QCT images. rBMD ratios (anterior/posterior, superior/mid-transverse, inferior/mid-transverse, and central/outer) were calculated from the centrum. A radiologist assigned a DHL severity score to adjacent intervertebral discs (L2-L3 and L3-L4). Int.BMD and Tb.BMD were both associated with age, though the decrease across age was greater in women (Int.BMD, - 2.6mg/cm3 per year; Tb.BMD, - 2.6mg/cm3 per year) than men (Int.BMD, - 0.5mg/cm3 per year; Tb.BMD, - 1.2mg/cm3 per year). The central/outer (- 0.027/decade) and superior/mid-transverse (- 0.018/decade) rBMD ratios were negatively associated with age, with similar trends in men and women. Higher Int.BMD or Tb.BMD was associated with increased odds of DHL after adjusting for age and sex. Low central/outer ratio and high anterior/poster and superior/mid-transverse ratios were also associated with increased odds of DHL. Our results indicate that the distribution of bone within the L3 vertebra is different across age, but not between sexes, and is associated with disc degeneration.

Mapping anisotropy improves QCT-based finite element estimation of hip strength in pooled stance and side-fall load configurations

Hip fractures are one of the most severe consequences of osteoporosis. Compared to the clinical standard of DXA-based aBMD at the femoral neck, QCT-based FEA delivers a better surrogate of femoral strength and gains acceptance for the calculation of hip fracture risk when a CT reconstruction is available. Isotropic, homogenised voxel-based, finite element (hvFE) models are widely used to estimate femoral strength in cross-sectional and longitudinal clinical studies. However, fabric anisotropy is a classical feature of the architecture of the proximal femur and the second determinant of the homogenised mechanical properties of trabecular bone. Due to the limited resolution, fabric anisotropy cannot be derived from clinical CT reconstructions. Alternatively, fabric anisotropy can be extracted from HR-pQCT images of cadaveric femora. In this study, fabric anisotropy from HR-pQCT images was mapped onto QCT-based hvFE models of 71 human proximal femora for which both HR-pQCT and QCT images were available. Stiffness and ultimate load computed from anisotropic hvFE models were compared with previous biomechanical tests in both stance and side-fall configurations. The influence of using the femur-specific versus a mean fabric distribution on the hvFE predictions was assessed. Femur-specific and mean fabric enhance the prediction of experimental ultimate force for the pooled, i.e. stance and side-fall, (isotropic: r2=0.81, femur-specific fabric: r2=0.88, mean fabric: r2=0.86,p<0.001) but not for the individual configurations. Fabric anisotropy significantly improves bone strength prediction for the pooled configurations, and mapped fabric provides a comparable prediction to true fabric. The mapping of fabric anisotropy is therefore expected to help generate more accurate QCT-based hvFE models of the proximal femur for personalised or multiple load configurations.

Comparison of proximal femur and vertebral body strength improvements in the FREEDOM trial using an alternative finite element methodology

Denosumab reduced the incidence of new fractures in postmenopausal women with osteoporosis by 68% at the spine and 40% at the hip over 36months compared with placebo in the FREEDOM study. This efficacy was supported by improvements from baseline in vertebral (18.2%) strength in axial compression and femoral (8.6%) strength in sideways fall configuration at 36months, estimated in Newtons by an established voxel-based finite element (FE) methodology. Since FE analyses rely on the choice of meshes, material properties, and boundary conditions, the aim of this study was to independently confirm and compare the effects of denosumab on vertebral and femoral strength during the FREEDOM trial using an alternative smooth FE methodology. Unlike the previous FE study, effects on femoral strength in physiological stance configuration were also examined. QCT data for the proximal femur and two lumbar vertebrae were analyzed by smooth FE methodology at baseline, 12, 24, and 36months for 51 treated (denosumab) and 47 control (placebo) subjects. QCT images were segmented and converted into smooth FE models to compute bone strength. L1 and L2 vertebral bodies were virtually loaded in axial compression and the proximal femora in both fall and stance configurations. Denosumab increased vertebral body strength by 10.8%, 14.0%, and 17.4% from baseline at 12, 24, and 36months, respectively (p<0.0001). Denosumab also increased femoral strength in the fall configuration by 4.3%, 5.1%, and 7.2% from baseline at 12, 24, and 36months, respectively (p<0.0001). Similar improvements were observed in the stance configuration with increases of 4.2%, 5.2%, and 5.2% from baseline (p≤0.0007). Differences between the increasing strengths with denosumab and the decreasing strengths with placebo were significant starting at 12months (vertebral and femoral fall) or 24months (femoral stance). Using an alternative smooth FE methodology, we confirmed the significant improvements in vertebral body and proximal femur strength previously observed with denosumab. Estimated increases in strength with denosumab and decreases with placebo were highly consistent between both FE techniques.

Bone Material Properties and Skeletal Fragility.

Deformations of vertebrae and sudden fractures of long bones caused by essentially normal loading are a characteristic problem in osteoporosis. If the loading is normal, then the explanation for and prediction of unexpected bone failure lies in understanding the mechanical properties of the whole bone-which come from its internal and external geometry, the mechanical properties of the hard tissue, and from how well the tissue repairs damage. Modern QCT and MRI imaging systems can measure the geometry of the mineralized tissue quite well in vivo-leaving the mechanical properties of the hard tissue and the ability of bone to repair damage as important unknown factors in predicting fractures. This review explains which material properties must be measured to understand why some bones fail unexpectedly despite our current ability to determine bone geometry and bone mineral content in vivo. Examples of how to measure the important mechanical properties are presented along with some analysis of potential drawbacks of each method. Particular attention is given to methods useful to characterize the loss of bone toughness caused by mechanical fatigue, drug side effects, and damage to the bone matrix.

Assessment of Transverse Isotropy in Clinical-Level CT Images of Trabecular Bone Using the Gradient Structure Tensor

The aim of this study was to develop a GST-based methodology for accurately measuring the degree of transverse isotropy in trabecular bone. Using femoral sub-regions scanned in high-resolution peripheral QCT (HR-pQCT) and clinical-level-resolution QCT, trabecular orientation was evaluated using the mean intercept length (MIL) and the gradient structure tensor (GST) on the HR-pQCT and QCT data, respectively. The influence of local degree of transverse isotropy (DTI) and bone mineral density (BMD) was incorporated into the investigation. In addition, a power based model was derived, rendering a 1:1 relationship between GST and MIL eigenvalues. A specific DTI threshold (DTI thres) was found for each investigated size of region of interest (ROI), above which the estimate of major trabecular direction of the GST deviated no more than 30° from the gold standard MIL in 95% of the remaining ROIs (mean error: 16°). An inverse relationship between ROI size and DTI thres was found for discrete ranges of BMD. A novel methodology has been developed, where transversal isotropic measures of trabecular bone can be obtained from clinical QCT images for a given ROI size, DTI thres and power coefficient. Including DTI may improve future clinical QCT finite-element predictions of bone strength and diagnoses of bone disease.

Identification of a crushable foam material model and application to strength and damage prediction of human femur and vertebral body

Finite element (FE) models allow quantitative predictions of bone strength and fracture location and, thus, became increasingly popular for assessing fracture risk or effectiveness of osteoporosis therapies. However, predictions crucially depend on the used material models, which are usually complex and rely on a large number of parameters. Therefore, the goal of this study was to propose a simple crushable foam (CF) material model and to perform an extensive comparison with data from the literature.Material parameters of the CF plasticity model were identified based on previously published yield stress data. Voxel-based FE models of thirty-six femora pairs and thirty-eight vertebral bodies were generated from QCT images. The femora models were analyzed with boundary conditions simulating one-legged stance and fall on the greater trochanter. The vertebral body models were subjected to uniaxial compression. Load-displacement curves, ultimate forces and damage distributions computed with the CF material model were compared to a reference material model as well as to in vitro experiments.The result showed that the FE models with CF material provided reasonable quantitative predictions of the ultimate forces measured in the experiments (R2>0.80). Comparison of the FE results obtained with CF and reference material model showed very similar outcomes regarding ultimate force, load-displacement behavior and damage patterns for all investigated anatomical sites and loading conditions.In conclusion, the identified CF material model provided good strength and damage predictions, required only few material parameters and is already implemented in many commercial FE solvers. Thus, it can be easily used in other studies.

Bone marrow lesions identified by MRI in knee osteoarthritis are associated with locally increased bone mineral density measured by QCT

Bone marrow lesions (BMLs) in the knee are associated with pain and compartment-specific joint space narrowing. However, the correlation of BMLs with bone mineral density (BMD) has rarely been investigated. The aim of the present study was to examine whether BMD in BMLs is altered compared to the surrounding bone. Thirty-four BMLs detected in osteoarthritis (OA) knees (KL grade 2 and 3) of 26 patients were investigated. A 3D-segmentation was used to determine BML volumes of interest (VOI) and their surrounding bone in MR images. These VOIs were registered to corresponding single-energy QCT images and a BMD analysis was performed. The same VOIs were transferred to control datasets (19 OA patients without BMLs) by an elastic registration, where the BMD analysis was repeated. To account for the dependence of bone marrow composition on BMD measures derived using single-energy QCT, simulations were performed to evaluate how changing fat-water compositions likely occurring with BML development may influence BMD measures and observed BMD differences between patients with and without BMLs. The association between loading in the knee and the occurrence of BMLs was investigated by medial to lateral (M:L) BMD ratios. BMD was significantly increased at BML locations, even with a fat-to-water conversion rate of 39%. The M:L BMD ratio was significantly increased in bones with medial BMLs. BMD was examined exactly at BML locations and surrounding bone using highly accurate segmentation and registration methods. BMD was significantly increased at BML locations (P<0.05).