Finite Element Analysis of the Hip and Spine Based on Quantitative Computed Tomography

Carpenter, R. Dana

doi:10.1007/s11914-013-0141-8

Cited by 21 publications

(14 citation statements)

References 42 publications

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“…A recent report reviewed the principles of the CT-based FE method and its application in the hip and spine. 11 In this paper, we aim to synthesize three recent validation experiments performed in our laboratory for patient-specific modeling of three skeletal sites at high risk of osteoporotic fractures: the distal radius, the vertebral body and the proximal femur. In particular, we quantify the predictive power of the voxel-based FE methodology for the image resolution available in vivo and compare it with the current densitometric surrogates of bone strength, bone mineral content (BMC) and areal bone mineral density (aBMD), for each anatomical site.…”

Section: Introductionmentioning

confidence: 99%

Finite element analysis for prediction of bone strength

Zysset¹,

Dall’Ara²,

Варга³

et al. 2013

BoneKEy Reports

138

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Finite element (FE) analysis has been applied for the past 40 years to simulate the mechanical behavior of bone. Although several validation studies have been performed on specific anatomical sites and load cases, this study aims to review the predictability of human bone strength at the three major osteoporotic fracture sites quantified in recently completed in vitro studies at our former institute. Specifically, the performance of FE analysis based on clinical computer tomography (QCT) is compared with the ones of the current densitometric standards, bone mineral content, bone mineral density (BMD) and areal BMD (aBMD). Clinical fractures were produced in monotonic axial compression of the distal radii, vertebral sections and in side loading of the proximal femora. QCT-based FE models of the three bones were developed to simulate as closely as possible the boundary conditions of each experiment. For all sites, the FE methodology exhibited the lowest errors and the highest correlations in predicting the experimental bone strength. Likely due to the improved CT image resolution, the quality of the FE prediction in the peripheral skeleton using high-resolution peripheral CT was superior to that in the axial skeleton with whole-body QCT. Because of its projective and scalar nature, the performance of aBMD in predicting bone strength depended on loading mode and was significantly inferior to FE in axial compression of radial or vertebral sections but not significantly inferior to FE in side loading of the femur. Considering the cumulated evidence from the published validation studies, it is concluded that FE models provide the most reliable surrogates of bone strength at any of the three fracture sites.

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Section: Introductionmentioning

confidence: 99%

Finite element analysis for prediction of bone strength

Zysset¹,

Dall’Ara²,

Варга³

et al. 2013

BoneKEy Reports

138

View full text Add to dashboard Cite

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“…Quantitative computed tomography (QCT)‐based finite element (FE) models are a promising tool for fracture risk assessment. These models can accommodate parameters related to the lesion, but can also cover additional aspects that play an important role in the assessment of the risk of fracture, such as the bone geometry, bone quality, and the daily loads applied to the bone . For example, the work by Whyne et al and Tschirhart et al studied the influence of tumor growth and loading scenario on precursors of burst fractures (e.g., load induced canal narrowing and vertebral bulge).…”

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confidence: 99%

Case‐specific non‐linear finite element models to predict failure behavior in two functional spinal units

Groenen

Bitter

Veluwen

et al. 2018

Journal Orthopaedic Research

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Current finite element (FE) models predicting failure behavior comprise single vertebrae, thereby neglecting the role of the posterior elements and intervertebral discs. Therefore, this study aimed to develop a more clinically relevant, case-specific non-linear FE model of two functional spinal units able to predict failure behavior in terms of (i) the vertebra predicted to fail; (ii) deformation of the specimens; (iii) stiffness; and (iv) load to failure. For this purpose, we also studied the effect of different bone density-mechanical properties relationships (material models) on the prediction of failure behavior. Twelve two functional spinal units (T6-T8, T9-T11, T12-L2, and L3-L5) with and without artificial metastases were destructively tested in axial compression. These experiments were simulated using CT-based case-specific non-linear FE models. Bone mechanical properties were assigned using four commonly used material models. In 10 of the 11 specimens our FE model was able to correctly indicate which vertebrae failed during the experiments. However, predictions of the three-dimensional deformations of the specimens were less promising. Whereas stiffness of the whole construct could be strongly predicted (R = 0.637-0.688, p < 0.01), we obtained weak correlations between FE predicted and experimentally determined load to failure, as defined by the total reaction force exhibiting a drop in force (R = 0.219-0.247, p > 0.05). Additionally, we found that the correlation between predicted and experimental fracture loads did not strongly depend on the material model implemented, but the stiffness predictions did. In conclusion, this work showed that, in its current state, our FE models may be used to identify the weakest vertebra, but that substantial improvements are required in order to quantify in vivo failure loads. © 2018 The Authors. Journal of Orthopaedic Research® Published by Wiley Periodical, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 9999:1-11, 2018.

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“…Theoretically, FEA should be the most powerful tool, as it integrates most of the biomechanical variables and it is based on well-established engineering mechanics theories and principles. Image-based FEA has advanced our understanding of bone biology and etiology of osteoporotic fracture [226][227][228]. In vitro studies have demonstrated that QCTbased FEA significantly improves the prediction of bone strength than densitometric measures [225,229].…”

Section: Links Between Biomechanical Variables and Clinical Risk Factorsmentioning

confidence: 99%

A biomechanical sorting of clinical risk factors affecting osteoporotic hip fracture

Luo

2015

Osteoporos Int

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Osteoporotic fracture has been found associated with many clinical risk factors, and the associations have been explored dominantly by evidence-based and case-control approaches. The major challenges emerging from the studies are the large number of the risk factors, the difficulty in quantification, the incomplete list, and the interdependence of the risk factors. A biomechanical sorting of the risk factors may shed lights on resolving the above issues. Based on the definition of load-strength ratio (LSR), we first identified the four biomechanical variables determining fracture risk, i.e., the risk of fall, impact force, bone quality, and bone geometry. Then, we explored the links between the FRAX clinical risk factors and the biomechanical variables by looking for evidences in the literature. To accurately assess fracture risk, none of the four biomechanical variables can be ignored and their values must be subject-specific. A clinical risk factor contributes to osteoporotic fracture by affecting one or more of the biomechanical variables. A biomechanical variable represents the integral effect from all the clinical risk factors linked to the variable. The clinical risk factors in FRAX mostly stand for bone quality. The other three biomechanical variables are not adequately represented by the clinical risk factors. From the biomechanical viewpoint, most clinical risk factors are interdependent to each other as they affect the same biomechanical variable(s). As biomechanical variables must be expressed in numbers before their use in calculating LSR, the numerical value of a biomechanical variable can be used as a gauge of the linked clinical risk factors to measure their integral effect on fracture risk, which may be more efficient than to study each individual risk factor.

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Finite Element Analysis of the Hip and Spine Based on Quantitative Computed Tomography

Cited by 21 publications

References 42 publications

Finite element analysis for prediction of bone strength

Finite element analysis for prediction of bone strength

Case‐specific non‐linear finite element models to predict failure behavior in two functional spinal units

A biomechanical sorting of clinical risk factors affecting osteoporotic hip fracture

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