Stepwise Validated Finite Element Model of the Human Lumbar Spine

Coombs, Dana; Bushelow, Michael; Laz, Peter J.; Rao, Milind; Rullkoetter, Paul J.

doi:10.1115/fmd2013-16167

Cited by 10 publications

(13 citation statements)

References 12 publications

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“…The bones and endplates were represented by 7470 elements total for each vertebra and were modeled as rigid, similar to several previous studies (Cegoñino et al, 2014;Coombs et al, 2013;Dreischarf et al, 2014;Little et al, 2007;Moramarco et al, 2010;Rao, 2012). Since the bone elements were rigid, their only function in the model was for visualization of the bone surface and confirmation of correct soft tissue attachment.…”

Section: Fe Model Detailsmentioning

confidence: 98%

“…Landmark data are then used as the basis for automated fitting of a pre-existing template FE mesh (Coombs et al, 2013;Rao, 2012) to the subject-specific bone geometry. All models are oriented with the vertical axis of L4 aligned with gravity.…”

Section: Fe Model Detailsmentioning

confidence: 99%

“…A detailed, quantitative mesh convergence study was conducted to verify appropriate mesh density created by the automated method. Semi-direct validation was performed by using calibrated material properties and precise boundary conditions for a single specimen from a prior experimental study (Coombs et al, 2013;Rao, 2012). A thorough indirect validation was also performed for 18 full lumbar FE models using generalized material properties and a range of experimental outcomes reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Automated finite element meshing of the lumbar spine: Verification and validation with 18 specimen-specific models

Campbell

Coombs

Rao³

et al. 2016

Journal of Biomechanics

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The purpose of this study was to seek broad verification and validation of human lumbar spine finite element models created using a previously published automated algorithm. The automated algorithm takes segmented CT scans of lumbar vertebrae, automatically identifies important landmarks and contact surfaces, and creates a finite element model. Mesh convergence was evaluated by examining changes in key output variables in response to mesh density. Semi-direct validation was performed by comparing experimental results for a single specimen to the automated finite element model results for that specimen with calibrated material properties from a prior study. Indirect validation was based on a comparison of results from automated finite element models of 18 individual specimens, all using one set of generalized material properties, to a range of data from the literature. A total of 216 simulations were run and compared to 186 experimental data ranges in all six primary bending modes up to 7.8Nm with follower loads up to 1000N. Mesh convergence results showed less than a 5% difference in key variables when the original mesh density was doubled. The semi-direct validation results showed that the automated method produced results comparable to manual finite element modeling methods. The indirect validation results showed a wide range of outcomes due to variations in the geometry alone. The studies showed that the automated models can be used to reliably evaluate lumbar spine biomechanics, specifically within our intended context of use: in pure bending modes, under relatively low non-injurious simulated in vivo loads, to predict torque rotation response, disc pressures, and facet forces.

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Section: Fe Model Detailsmentioning

confidence: 98%

Section: Fe Model Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated finite element meshing of the lumbar spine: Verification and validation with 18 specimen-specific models

Campbell

Coombs

Rao³

et al. 2016

Journal of Biomechanics

View full text Add to dashboard Cite

show abstract

“…A detailed, quantitative mesh convergence study was conducted to verify appropriate mesh density created by the automated method. Direct validation was performed by using calibrated material properties and precise boundary conditions for a single specimen from a prior experimental study (Coombs et al, 2013;Rao, 2012). A thorough indirect validation was also performed for 18 full lumbar FE models using generalized material properties and a range of experimental outcomes reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

Automated finite element modeling of the lumbar spine: Using a statistical shape model to generate a virtual population of models

Campbell

Petrella

2016

Journal of Biomechanics

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Population-based modeling of the lumbar spine has the potential to be a powerful clinical tool. However, developing a fully parameterized model of the lumbar spine with accurate geometry has remained a challenge. The current study used automated methods for landmark identification to create a statistical shape model of the lumbar spine. The shape model was evaluated using compactness, generalization ability, and specificity. The primary shape modes were analyzed visually, quantitatively, and biomechanically. The biomechanical analysis was performed by using the statistical shape model with an automated method for finite element model generation to create a fully parameterized finite element model of the lumbar spine. Functional finite element models of the mean shape and the extreme shapes (±3 standard deviations) of all 17 shape modes were created demonstrating the robust nature of the methods. This study represents an advancement in finite element modeling of the lumbar spine and will allow population-based modeling in the future.

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“…This experimental approach has been adopted to quantify laxity in the spine [66] and the knee [67]. Though these data to date have principally been used to validate patient-specific finite element models of the corresponding joint [68][69][70], the opportunity exists to leverage statistical modeling to quantify the interdependencies between modes of laxity (e.g., flexion/extension, lateral bending, and axial rotation in the spine), nonlinear properties of individual constituents (e.g., ligaments and capsules in the spine), or properties of adjacent joints. Figure 4 demonstrates an application of statistical modeling for knee laxity utilizing previously unpublished data.…”

Section: Materials Propertiesmentioning

confidence: 99%

Incorporating Population-Level Variability in Orthopedic Biomechanical Analysis: A Review

Bischoff¹,

Dai²,

Goodlett

et al. 2014

Journal of Biomechanical Engineering

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Effectively addressing population-level variability within orthopedic analyses requires robust data sets that span the target population and can be greatly facilitated by statistical methods for incorporating such data into functional biomechanical models. Data sets continue to be disseminated that include not just anatomical information but also key mechanical data including tissue or joint stiffness, gait patterns, and other inputs relevant to analysis of joint function across a range of anatomies and physiologies. Statistical modeling can be used to establish correlations between a variety of structural and functional biometrics rooted in these data and to quantify how these correlations change from health to disease and, finally, to joint reconstruction or other clinical intervention. Principal component analysis provides a basis for effectively and efficiently integrating variability in anatomy, tissue properties, joint kinetics, and kinematics into mechanistic models of joint function. With such models, bioengineers are able to study the effects of variability on biomechanical performance, not just on a patient-specific basis but in a way that may be predictive of a larger patient population. The goal of this paper is to demonstrate the broad use of statistical modeling within orthopedics and to discuss ways to continue to leverage these techniques to improve biomechanical understanding of orthopedic systems across populations.

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Stepwise Validated Finite Element Model of the Human Lumbar Spine

Cited by 10 publications

References 12 publications

Automated finite element meshing of the lumbar spine: Verification and validation with 18 specimen-specific models

Automated finite element meshing of the lumbar spine: Verification and validation with 18 specimen-specific models

Automated finite element modeling of the lumbar spine: Using a statistical shape model to generate a virtual population of models

Incorporating Population-Level Variability in Orthopedic Biomechanical Analysis: A Review

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