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Using inverse finite element analysis to identify spinal tissue behaviour in situ.

ABSTRACT: In computational modelling of musculoskeletal applications, one of the critical aspects is ensuring that a model can capture intrinsic population variability and not only representative of a "mean" individual. Developing and calibrating models with this aspect in mind is key for the credibility of a modelling methodology. This often requires calibration of complex models with respect to 3D experiments and measurements on a range of specimens or patients. Most Finite Element (FE) software's do not have such a capacity embedded in their core tools. This paper presents a versatile interface between Finite Element (FE) software and optimisation tools, enabling calibration of a group of FE models on a range of experimental data. It is provided as a Python toolbox which has been fully tested and verified on Windows platforms. The toolbox is tested in three case studies involving in vitro testing of spinal tissues.

SUBMITTER: Mengoni M

PROVIDER: S-EPMC7884930 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Using inverse finite element analysis to identify spinal tissue behaviour in situ.

Mengoni Marlène M

Methods (San Diego, Calif.) 20200206

In computational modelling of musculoskeletal applications, one of the critical aspects is ensuring that a model can capture intrinsic population variability and not only representative of a "mean" individual. Developing and calibrating models with this aspect in mind is key for the credibility of a modelling methodology. This often requires calibration of complex models with respect to 3D experiments and measurements on a range of specimens or patients. Most Finite Element (FE) software's do no ...[more]

PMID: 32036039

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Project description:Central to the investigation of the biomechanics of traumatic brain injury (TBI) and the assessment of injury risk from head impact are finite element (FE) models of the human brain. However, many existing FE human brain models have been developed with simplified representations of the parenchyma, which may limit their applicability as an injury prediction tool. Recent advances in neuroimaging techniques and brain biomechanics provide new and necessary experimental data that can improve the biofidelity of FE brain models. In this study, the CAB-20MSym template model was developed, calibrated, and extensively verified. To implement material heterogeneity, a magnetic resonance elastography (MRE) template image was leveraged to define the relative stiffness gradient of the brain model. A multi-stage inverse FE (iFE) approach was used to calibrate the material parameters that defined the underlying non-linear deviatoric response by minimizing the error between model-predicted brain displacements and experimental displacement data. This process involved calibrating the infinitesimal shear modulus of the material using low-severity, low-deformation impact cases and the material non-linearity using high-severity, high-deformation cases from a dataset of in situ brain displacements obtained from cadaveric specimens. To minimize the geometric discrepancy between the FE models used in the iFE calibration and the cadaveric specimens from which the experimental data were obtained, subject-specific models of these cadaveric brain specimens were developed and used in the calibration process. Finally, the calibrated material parameters were extensively verified using independent brain displacement data from 33 rotational head impacts, spanning multiple loading directions (sagittal, coronal, axial), magnitudes (20-40 rad/s), durations (30-60 ms), and severity. Overall, the heterogeneous CAB-20MSym template model demonstrated good biofidelity with a mean overall CORA score of 0.63 ± 0.06 when compared to in situ brain displacement data. Strains predicted by the calibrated model under non-injurious rotational impacts in human volunteers (N = 6) also demonstrated similar biofidelity compared to in vivo measurements obtained from tagged magnetic resonance imaging studies. In addition to serving as an anatomically accurate model for further investigations of TBI biomechanics, the MRE-based framework for implementing material heterogeneity could serve as a foundation for incorporating subject-specific material properties in future models.

Project description:Prior studies show vertebral strength from computed tomography-based finite element analysis may be associated with vertebral fracture risk. We found vertebral strength had a strong association with new vertebral fractures, suggesting that vertebral strength measures identify those at risk for vertebral fracture and may be a useful clinical tool.IntroductionWe aimed to determine the association between vertebral strength by quantitative computed tomography (CT)-based finite element analysis (FEA) and incident vertebral fracture (VF). In addition, we examined sensitivity and specificity of previously proposed diagnostic thresholds for fragile bone strength and low BMD in predicting VF.MethodsIn a case-control study, 26 incident VF cases (13 men, 13 women) and 62 age- and sex-matched controls aged 50 to 85 years were selected from the Framingham multi-detector computed tomography cohort. Vertebral compressive strength, integral vBMD, trabecular vBMD, CT-based BMC, and CT-based aBMD were measured from CT scans of the lumbar spine.ResultsLower vertebral strength at baseline was associated with an increased risk of new or worsening VF after adjusting for age, BMI, and prevalent VF status (odds ratio (OR) = 5.2 per 1 SD decrease, 95% CI 1.3-19.8). Area under receiver operating characteristic (ROC) curve comparisons revealed that vertebral strength better predicted incident VF than CT-based aBMD (AUC = 0.804 vs. 0.715, p = 0.05) but was not better than integral vBMD (AUC = 0.815) or CT-based BMC (AUC = 0.794). Additionally, proposed fragile bone strength thresholds trended toward better sensitivity for identifying VF than that of aBMD-classified osteoporosis (0.46 vs. 0.23, p = 0.09).ConclusionThis study shows an association between vertebral strength measures and incident vertebral fracture in men and women. Though limited by a small sample size, our findings also suggest that bone strength estimates by CT-based FEA provide equivalent or better ability to predict incident vertebral fracture compared to CT-based aBMD. Our study confirms that CT-based estimates of vertebral strength from FEA are useful for identifying patients who are at high risk for vertebral fracture.

Dataset Information

Using inverse finite element analysis to identify spinal tissue behaviour in situ.

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Using inverse finite element analysis to identify spinal tissue behaviour in situ.

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