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.
Highlights
In the area of pre-clinical testing of medical devices, in silico modelling is a promising tool, with the ability to model very specific situations, to account for disease-specific tissue behaviour, and more generally to provide a testing platform that can account for the large variation in the population [1]
The Brent optimisation led to improvements in the RMS difference and in the one-to-one difference of all but two samples (Fig. 3), reducing the RMS error from 18.4% to 9.7% and maximal one-to-one error from 31% to 23%
The developed toolbox is composed of two classes defining objects respectively for the objective function and the optimisation process
Summary
In the area of pre-clinical testing of medical devices, in silico modelling is a promising tool, with the ability to model very specific situations, to account for disease-specific tissue behaviour, and more generally to provide a testing platform that can account for the large variation in the population [1]. In a purely mechanical point of view, natural tissue behaviour is often modelled with a phenomenological approach [5,6]. A phenomenological model has the benefit of being relatively simple with respect to the description of complex tissues, with the disadvantage that parameters are not directly measurable and need to be calibrated to match experimental data. Because natural tissues are anisotropic, hydrated, and with in situ pre-strains [6,7,8,9], conducting experimental analysis with standard mechanical tests in order to directly derive a stress/strain behaviour is often not representative of the physiological behaviour of the tissue. The calibration of constitutive models directly from experimental data is not always possible or relevant
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