In Vivo Assessment of Age‐ and Loading Configuration‐Related Changes in Multiscale Mechanical Behavior of the Human Proximal Femur Using MRI‐Based Finite Element Analysis

Biomechanical MRI detects reduced bone strength in subjects with vertebral fractures

Vertebral fracture is one of the most serious consequences of osteoporosis. Estimation of vertebral strength from magnetic resonance imaging (MRI) scans may provide a new approach for the prediction of vertebral fractures. To that end, we sought to establish a biomechanical MRI (BMRI) method to compute vertebral strength and test its ability to distinguish fracture from non-fracture subjects. This case-control study included 30 subjects without vertebral fractures and 15 subjects with vertebral fractures. All subjects underwent MRI with a mDIXON-Quant sequence and quantitative computed tomography (QCT), from which proton fat fraction-based bone marrow adipose tissue (BMAT) content and volumetric bone mineral density (vBMD) were measured, respectively. Nonlinear finite element analysis was applied to MRI and QCT scans of L2 vertebrae to compute vertebral strength (BMRI- and BCT-strength). The differences in BMAT content, vBMD, BMRI-strength and BCT-strength between the two groups were examined by t-tests. Receiver operating characteristic (ROC) analysis was performed to assess the ability of each measured parameter to distinguish fracture from non-fracture subjects. Results showed that the fracture group had 23 % lower BMRI-strength (P < .001) and 19 % higher BMAT content (P < .001) than the non-fracture group, whereas no significant difference in vBMD was detected between the two groups. A poor correlation was found between vBMD and BMRI-strength (R² = 0.33). Compared to vBMD and BMAT content, BMRI- and BCT-strength had the larger area under the curve (0.82 and 0.84, respectively) and provided better sensitivity and specificity in separating fracture from non-fracture subjects. In conclusion, BMRI is capable of detecting reduced bone strength in patients with vertebral fracture, and may serve as a new approach for risk assessment of vertebral fracture.

The predictive ability of a QCT-FE model of the proximal femoral stiffness under multiple load cases is strongly influenced by experimental uncertainties

Despite significant improvements in terms of the predictive ability of Quantitative Computed Tomography based Finite Element (QCT-FE) models in estimating femoral strength (fracture load and stiffness), no substantial clinical adoption of this method has taken place to date. Narrowing the wide variability of FE results by standardizing the methodology and validation protocols, as well as reducing the uncertainties in the FEA process have been proposed as routes towards improved reliability. The aim of this study was to: First, validate a QCT-FE model of proximal femoral stiffness in multiple stance load cases, and second, using a parametric approach, determine the influence of select experimental and modeling parameters on the predictive ability of our model. Ten fresh frozen human femoral samples were tested in neutral stance, 15° adducted and 15° abducted load cases. Voxel-based linear-elastic QCT-FE models of the samples were generated to predict the models' stiffness values in all load cases. The base FE models were validated against the experimental results using linear regression. Thirty six deviated models were created using the minimum and maximum values of experiment-based "plausible range" for 18 parameters in 4 categories of embedding, loading, material, and segmentation. The predictive ability of the models were compared in terms of the coefficient of determination (R²) of the linear regression between the measured and predicted stiffness values in all load cases. Our model was capable of capturing 90% of the variation in the experimental stiffness of the samples in neutral stance position (R² = 0.9, concordance correlation coefficient (CCC) = 0.93, percent root mean squared error (RMSE%) = 8.4%, slope and intercept not significantly different from unity and zero, respectively). Embedding and loading categories strongly affected the predictive ability of the models with an average percent difference in R² of 4.36% ± 2.77 and 2.96% ± 1.69 for the stance-neutral load case, respectively. The performance of the models were significantly different in adducted and abducted load cases with their R² dropping to 71% and 70%, respectively. Similarly, off-axes load cases were affected by the parameters differently compared to the neutral load case, with the loading parameter category imposing more than 10% difference on their R², larger than all other categories. We also showed that automatically selecting the best performing plausible value for each parameter and each sample would result in a perfectly linear correlation (R²> 0.99) between the "tuned" model's predicted stiffness and experimental results. Based on our results, high sensitivity of the model performance to experimental parameters requires extra diligence in modeling the embedding geometry and the loading angles since these sources of uncertainty could dwarf the effects of material modeling and image processing parameters. The results of this study could help in improving the robustness of the QCT-FE models of proximal femur by limiting the uncertainties in the experimental and modeling steps.

Finite Element Assessment of Bone Fragility from Clinical Images

PURPOSE OF REVIEW

We re-evaluated clinical applications of image-to-FE models to understand if clinical advantages are already evident, which proposals are promising, and which questions are still open.

RECENT FINDINGS

CT-to-FE is useful in longitudinal treatment evaluation and groups discrimination. In metastatic lesions, CT-to-FE strength alone accurately predicts impending femoral fractures. In osteoporosis, strength from CT-to-FE or DXA-to-FE predicts incident fractures similarly to DXA-aBMD. Coupling loads and strength (possibly in dynamic models) may improve prediction. One promising MRI-to-FE workflow may now be tested on clinical data. Evidence of artificial intelligence usefulness is appearing. CT-to-FE is already clinical in opportunistic CT screening for osteoporosis, and risk of metastasis-related impending fractures. Short-term keys to improve image-to-FE in osteoporosis may be coupling FE with fall risk estimates, pool FE results with other parameters through robust artificial intelligence approaches, and increase reproducibility and cross-validation of models. Modeling bone modifications over time and bone fracture mechanics are still open issues.

Experimental testing and biomechanical CT analysis of Chinese cadaveric vertebrae with different modeling approaches

Osteoporosis is characterized by reduced bone strength predisposing to an increased risk of fracture. Biomechanical computed tomography (BCT), predicting bone strength via CT-based finite element analysis (FEA), is now clinically available in the USA for diagnosing osteoporosis or assessing fracture risk. However, it has not been previously validated using a cohort of only Chinese subjects. Additionally, the effect of various modeling approaches on BCT outcomes remains elusive. To address these issues, we performed DXA and QCT scanning, compression testing, and BCT analyses on thirteen vertebrae derived from Chinese donors. Three BCT models were created (voxBCT and tetBCT: voxel-based and tetrahedral element-based FE models generated by a commercial software; matBCT: tetrahedral element-based FE model generated by a custom MATLAB program). BCT-computed outcomes were compared with experimental measures or between different BCT models. Results showed that, DXA-measured areal bone mineral density (aBMD) showed weak correlations with experimentally-measured vertebral stiffness (R² = 0.28) and strength (R² = 0.34). Compared to DXA-aBMD, BCT-computed stiffness provided improved correlations with experimentally-measured stiffness (voxBCT: R² = 0.82; tetBCT: R² = 0.77; matBCT: R² = 0.76) and strength (voxBCT: R² = 0.55; tetBCT: R² = 0.57; matBCT: R² = 0.53); BCT-computed mechanical parameters (stiffness, stress and strain) of the three different models were highly correlated with each other, with coefficient of determination (R²) values of 0.89-0.98. These results, based on a cohort of Chinese vertebral cadavers, suggest that BCT is superior over aBMD to consistently predict vertebral mechanical characteristics, regardless of the modeling approaches of choice.