Statistical shape and appearance models for fast and automated estimation of proximal femur fracture load using 2D finite element models

Sarkalkan, Nazlı; Waarsing, J.H.; Bos, P.K.; Weinans, Harrie; Zadpoor, Amir A.

doi:10.1016/j.jbiomech.2014.06.027

Cited by 19 publications

(9 citation statements)

References 39 publications

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“…Statistical models have been used to study the variation of bone properties, improve implant design and build patient-specific FE models [26][27][28][29][30]. However existing models focused on bone shape and to some extent to bone density, but overlooked the anisotropy of trabecular bone.…”

Section: Discussionmentioning

confidence: 99%

Statistical analysis of the inter-individual variations of the bone shape, volume fraction and fabric and their correlations in the proximal femur

Taghizadeh

Chandran

Zysset

et al. 2017

Bone

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Including structural information of trabecular bone improves the prediction of bone strength and fracture risk. However, this information is available in clinical CT scans, only for peripheral bones. We hypothesized that a correlation exists between the shape of the bone, its volume fraction (BV/TV) and fabric, which could be characterized using statistical modeling. High-resolution peripheral computed tomography (HR-pQCT) images of 73 proximal femurs were used to build a combined statistical model of shape, BV/TV and fabric. The model was based on correspondence established by image registration and by morphing of a finite element mesh describing the spatial distribution of the bone properties. Results showed no correlation between the distribution of bone shape, BV/TV and fabric. Only the first mode of variation associated with density and orientation showed a strong relationship (R>0.8). In addition, the model showed that the anisotropic information of the proximal femur does not vary significantly in a population of healthy, osteoporotic and osteopenic samples. In our dataset, the average anisotropy of the population was able to provide a close approximation of the patient-specific anisotropy. These results were confirmed by homogenized finite element (hFE) analyses, which showed that the biomechanical behavior of the proximal femur was not significantly different when the average anisotropic information of the population was used instead of patient-specific fabric extracted from HR-pQCT. Based on these findings, it can be assumed that the fabric information of the proximal femur follows a similar structure in an elderly population of healthy, osteopenic and osteoporotic proximal femurs.

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Section: Discussionmentioning

confidence: 99%

Statistical analysis of the inter-individual variations of the bone shape, volume fraction and fabric and their correlations in the proximal femur

Taghizadeh

Chandran

Zysset

et al. 2017

Bone

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“…Recently a novel approach to generate 3D models from combination of statistical shape models and 2D DXA scans has been developed [40,41] and could be an appealing solution for the further developments of subject specific models based on clinical DXA images but with 3D applications.…”

Section: Discussionmentioning

confidence: 99%

Experimental validation of DXA-based finite element models for prediction of femoral strength

Dall’Ara

Eastell

Viceconti

et al. 2016

Journal of the Mechanical Behavior of Biomedical Materials

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Osteoporotic fractures are a major clinical problem and current diagnostic tools have an accuracy of only 50%. The aim of this study was to validate dual energy X-rays absorptiometry (DXA)-based finite element (FE) models to predict femoral strength in two loading configurations. Thirty-six pairs of fresh frozen human proximal femora were scanned with DXA and quantitative computed tomography (QCT). For each pair one femur was tested until failure in a one-legged standing configuration (STANCE) and one by replicating the position of the femur in a fall onto the greater trochanter (SIDE). Subject-specific 2D DXA-based linear FE models and 3D QCT-based nonlinear FE models were generated for each specimen and used to predict the measured femoral strength. The outcomes of the models were compared to standard DXA-based areal bone mineral density (aBMD) measurements. For the STANCE configuration the DXA-based FE models (R(2)=0.74, SEE=1473N) outperformed the best densitometric predictor (Neck_aBMD, R(2)=0.66, SEE=1687N) but not the QCT-based FE models (R(2)=0.80, SEE=1314N). For the SIDE configuration both QCT-based FE models (R(2)=0.85, SEE=455N) and DXA neck aBMD (R(2)=0.80, SEE=502N) outperformed DXA-based FE models (R(2)=0.77, SEE=529N). In both configurations the DXA-based FE model provided a good 1:1 agreement with the experimental data (CC=0.87 for SIDE and CC=0.86 for STANCE), with proper optimization of the failure criteria. In conclusion we found that the DXA-based FE models are a good predictor of femoral strength as compared with experimental data ex vivo. However, it remains to be investigated whether this novel approach can provide good predictions of the risk of fracture in vivo.

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“…107 We have recently started the development of such automated FE modeling techniques. 112 Automated segmentation of images and assignment of mechanical properties based on the automatically segmented density distribution could facilitate the clinical adoption of the fracture risk assessment tools based on FE modeling, because there will be no need for having technical expertise in every hospital where the fracture risk assessment modeling tools are going to be used. To achieve this, it is necessary for FE modeling specialists to collaborate with specialists from other disciplines particularly medical image processing specialists to facilitate the generation of automated FE modeling tools and, thereby, improve the chance of clinical acceptance of FE models as a viable fracture risk assessment tool.…”

Section: Shape Analysis and Statistical Shape Modelsmentioning

confidence: 99%

Assessment of Osteoporotic Femoral Fracture Risk: Finite Element Method as a Potential Replacement for Current Clinical Techniques

Munckhof

Nikooyan

Zadpoor

2015

J. Mech. Med. Biol.

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Femoral fracture risk prediction is a necessary step preceding effective pharmacological intervention or pre-operative planning. Current clinical methods for fracture risk prediction rely on 2D imaging methods and have limited predictive value. Researchers are therefore trying to find improved methods for fracture prediction. During last few decades, many studies have focused on integration of 3D imaging techniques and the finite element (FE) method to improve the accuracy of fracture assessment techniques. In this paper, we review the recent advances in FE and other techniques for predicting the risk of femoral fractures. Based on a number of selected studies, the different steps that are involved in generation of patient-specific FE models are reviewed with particular emphasis on the fracture criteria. The inaccuracies that might arise due to the imperfections of the involved steps are also discussed. It is concluded that compared to image-and geometry-based techniques, FE is a more promising approach for prediction of fracture loads. However, certain technological advancements in FE modeling protocols are required before FE modeling can be recruited in clinical settings.

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Statistical shape and appearance models for fast and automated estimation of proximal femur fracture load using 2D finite element models

Cited by 19 publications

References 39 publications

Statistical analysis of the inter-individual variations of the bone shape, volume fraction and fabric and their correlations in the proximal femur

Statistical analysis of the inter-individual variations of the bone shape, volume fraction and fabric and their correlations in the proximal femur

Experimental validation of DXA-based finite element models for prediction of femoral strength

Assessment of Osteoporotic Femoral Fracture Risk: Finite Element Method as a Potential Replacement for Current Clinical Techniques

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