Computational finite element bone mechanics accurately predicts mechanical competence in the human radius of an elderly population

Mueller, Timothy I.; Christen, D. K.; Sandercott, S.D.; Boyd, Steven K.; Rietbergen, Bert van; Eckstein, F.; Lochmüller, Eva-Maria; Müller, Ralph; Lenthe, G. Harry van

doi:10.1016/j.bone.2011.02.022

Cited by 109 publications

(107 citation statements)

References 27 publications

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“…In a follow-up study using the same 0.007 effective strain threshold for element failure, a nonlinear relationship was observed for critical failure volume (0.1-50% of model volume) versus the error between predicted and experimentally measured strength of embalmed human distal radii, which were scanned at a nominal resolution of 89 mm (compared with 165 mm from the previous study) with a lower tissue modulus of 6.829 GPa. 19 Like the present study involving mouse VBs, these studies found that the optimum failure volume for a homogeneous material definition was between 1 and 10%. 18,19 Conceivably, for any given study, there is an optimum failure volume and an optimum failure strain that maximizes R 2 and minimizes RMSE, respectively.…”

Section: Discussionsupporting

confidence: 51%

“…19 Like the present study involving mouse VBs, these studies found that the optimum failure volume for a homogeneous material definition was between 1 and 10%. 18,19 Conceivably, for any given study, there is an optimum failure volume and an optimum failure strain that maximizes R 2 and minimizes RMSE, respectively. However, the data presented here demonstrate that achieving this optimum is not essential to detect group-wise differences in whole-bone strength that are greater than 20% ( Table 3).…”

Section: Discussionsupporting

confidence: 51%

“…7,8 Moreover, as evidence of their ability to make such predictions, QCT-FEAs can differentiate fracture patients from non-fracture patients, although some overlap in predicted strength exists across the cohorts. [9][10][11][12][13][14] Much of the validation behind the failure criteria in these FE model predictions came from correlations with strength measurements as determined by whole bone testing of cadaveric tissue from large animals and humans, namely the proximal femur, distal radius and vertebra, [15][16][17][18][19] whereas little validation has been performed on murine bone. Material assumptions are based on a number of published empirical relationships that either (i) convert volumetric mineral density to tissue modulus (E t ) 20 in which the attenuation in CT is converted to density using a hydroxyapatite (HA) phantom and elastic tissue modulus (E t ) is derived from local measurements by nanoindentation, (ii) relate QCT density to ash density 21 and then convert ash density to apparent-level material properties for which different empirical relationships exist for different directions of loading (compression versus tension) and bone type (cortical versus trabecular) 17,22 or (iii) relate QCT volumetric density directly to apparent-level properties using empirical relationships developed from cadaveric experiments.…”

Section: Introductionmentioning

confidence: 99%

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Predicting mouse vertebra strength with micro-computed tomography-derived finite element analysis

Nyman¹,

Uppuganti²,

Makowski³

et al. 2015

BoneKEy Reports

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As in clinical studies, finite element analysis (FEA) developed from computed tomography (CT) images of bones are useful in pre-clinical rodent studies assessing treatment effects on vertebral body (VB) strength. Since strength predictions from microCT-derived FEAs (mFEA) have not been validated against experimental measurements of mouse VB strength, a parametric analysis exploring material and failure definitions was performed to determine whether elastic mFEAs with linear failure criteria could reasonably assess VB strength in two studies, treatment and genetic, with differences in bone volume fraction between the control and the experimental groups. VBs were scanned with a 12-mm voxel size, and voxels were directly converted to 8-node, hexahedral elements. The coefficient of determination or R 2 between predicted VB strength and experimental VB strength, as determined from compression tests, was 62.3% for the treatment study and 85.3% for the genetic study when using a homogenous tissue modulus (E t ) of 18 GPa for all elements, a failure volume of 2%, and an equivalent failure strain of 0.007. The difference between prediction and measurement (that is, error) increased when lowering the failure volume to 0.1% or increasing it to 4%. Using inhomogeneous tissue density-specific moduli improved the R 2 between predicted and experimental strength when compared with uniform E t ¼ 18 GPa. Also, the optimum failure volume is higher for the inhomogeneous than for the homogeneous material definition. Regardless of model assumptions, mFEA can assess differences in murine VB strength between experimental groups when the expected difference in strength is at least 20%.

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Section: Discussionsupporting

confidence: 51%

Section: Discussionsupporting

confidence: 51%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting mouse vertebra strength with micro-computed tomography-derived finite element analysis

Nyman¹,

Uppuganti²,

Makowski³

et al. 2015

BoneKEy Reports

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“…[16][17][18] Stiffness, apparent modulus, cortical compartment load fraction and failure load estimate were computed for each model.…”

Section: Q4 Fes-rowing Attenuates Bone Loss In Sci Rs Gibbons Et Almentioning

confidence: 99%

FES-rowing attenuates bone loss following spinal cord injury as assessed by HR-pQCT

Gibbons

Beaupré

Kazakia

2016

Spinal Cord Ser Cases

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Neurologically motor complete spinal cord injury (SCI) presents a unique model of bone loss whereby specific regional sites are exposed to a complete loss of voluntary muscle-induced skeletal loading against gravity. This results in a high rate of bone loss, especially in the lower limbs where trabecular bone mass decreases by~50-60% and cortical bone mass decreases by 25-34% before the rate of bone loss slows. These SCI-induced losses that are likely superimposed on continual age-related bone losses, increase the risk of low-impact fragility fracture. The fracture incidence 20 years post SCI is reported to be 4.6% per year. An intervention that effectively prevents, attenuates, or reverses bone loss is therefore highly desirable. We present a case study of an individual with chronic complete SCI, where bone loss has been attenuated following long-term functional electrical stimulation (FES)-rowing training. In this case study, we characterize the ultradistal tibia and ultradistal radius of the FES-rower with chronic complete SCI using high-resolution-peripheral quantitative computed tomography. These data are compared with a group of FESuntrained individuals with chronic complete SCI and to a normative non-SCI cohort. The evidence suggests, albeit from a single individual, that long-term FES-rowing training can attenuate bone loss secondary to chronic complete SCI. Indeed, key FES-rower's bone metrics for the ultradistal tibia more closely resemble normative age-matched values, which may have clinical significance since the majority of fragility fractures in chronic SCI occur in the lower extremities. Neurologically motor complete spinal cord injury (SCI) presents a unique model of bone loss whereby specific regional sites are exposed to a complete loss of voluntary muscle-induced skeletal loading against gravity. The initial loss of bone mineral content is estimated to approach 4% per month in trabecular sites, and 2% per month in cortical sites. Spinal Cord Series and Cases1 Trabecular bone mass is estimated to decrease by 48% of pre-injury values in the femur, reaching a new slower rate of loss by 3 years and 58% in the tibia by 5 years. Cortical bone mass decreases by 34% of pre-injury values in the femur by 5 years, and 25% in the tibia by 7 years.2 These losses are likely superimposed on continual age-related bone loss.3 SCIinduced bone loss often leads to the development of osteoporosis, which increases the risk of low-impact fragility fracture. 4 The fracture incidence 20 years post SCI is reported to be 4.6% per year.5 Thus, even active individuals with complete SCI at age 20 undergoing conventional standard of care have a very high likelihood of sustaining a fragility fracture in their lifetime, markedly affecting quality of life and increasing the risk of mortality. 6 An intervention that effectively prevents, attenuates or reverses bone loss is therefore highly desirable. 7One such intervention under investigation is functional electrical stimulation (FES) rowing. 8 In the present configuration, FES...

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“…23,24 We therefore posit that nonlinear μFEA might be a more powerful tool to predict fracture risk. 21,[25][26][27][28] A few studies showed the ability of nonlinear μFEA to predict tensile and compressive yield properties of bone. 23,28,29 However, there is little work aimed at predicting trabecular bone post-yield behavior through nonlinear analysis.…”

Section: Introductionmentioning

confidence: 99%

Potential of in vivo MRI‐based nonlinear finite‐element analysis for the assessment of trabecular bone post‐yield properties

et al. 2013

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Purpose: Bone strength is the key factor impacting fracture risk. Assessment of bone strength from high-resolution (HR) images have largely relied on linear micro-finite element analysis (μFEA) even though failure always occurs beyond the yield point, which is outside the linear regime. Nonlinear μFEA may therefore be more informative in predicting failure behavior. However, existing nonlinear models applied to trabecular bone (TB) have largely been confined to micro-computed tomography (μCT) and, more recently, HR peripheral quantitative computed tomography (HR-pQCT) images, and typically have ignored evaluation of the post-yield behavior. The primary purpose of this work was threefold: (1) to provide an improved algorithm and program to assess TB yield as well as post-yield properties; (2) to explore the potential benefits of nonlinear μFEA beyond its linear counterpart; and (3) to assess the feasibility and practicality of performing nonlinear analysis on desktop computers on the basis of micro-magnetic resonance (μMR) images obtained in vivo in patients. Methods: A method for nonlinear μFE modeling of TB yield as well as post-yield behavior has been designed where material nonlinearity is captured by adjusting the tissue modulus iteratively according to the tissue-level effective strain obtained from linear analysis using a computationally optimized algorithm. The software allows for images at in vivo μMRI resolution as input with retention of grayscale information. Associations between axial stiffness estimated from linear analysis and yield as well as post-yield parameters from nonlinear analysis were investigated from in vivo μMR images of the distal tibia (N = 20; ages: 58-84) and radius (N = 20; ages: 50-75). Results: All simulations were completed in 1 h or less for 61 strain levels using a desktop computer (dual quad-core Xeon 3.16 GHz CPUs equipped with 40 GB of RAM). Although yield stress and ultimate stress correlated strongly (R 2 > 0.95, p < 0.001) with axial stiffness, toughness correlated moderately at the distal tibia (R 2 = 0.81, p < 0.001) and only weakly at the distal radius (R 2 = 0.34, p = 0.007). Further, toughness was found to vary by up to 16% for bone of very similar axial stiffness (<2%). Conclusions:The work demonstrates the practicality of nonlinear μFE simulations at in vivo μMRI resolution, as well as its potential for providing additional information beyond that obtainable from linear analysis. The data suggest that a direct assessment of toughness may provide information not captured by stiffness.

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Computational finite element bone mechanics accurately predicts mechanical competence in the human radius of an elderly population

Cited by 109 publications

References 27 publications

Predicting mouse vertebra strength with micro-computed tomography-derived finite element analysis

Predicting mouse vertebra strength with micro-computed tomography-derived finite element analysis

FES-rowing attenuates bone loss following spinal cord injury as assessed by HR-pQCT

Potential of in vivo MRI‐based nonlinear finite‐element analysis for the assessment of trabecular bone post‐yield properties

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