Mesh morphing and response surface analysis: quantifying sensitivity of vertebral mechanical behavior

Morphing the feature-based multi-blocks of normative/healthy vertebral geometries to scoliosis vertebral geometries: development of personalized finite element models

Personalized Finite Element (FE) models and hexahedral elements are preferred for biomechanical investigations. Feature-based multi-block methods are used to develop anatomically accurate personalized FE models with hexahedral mesh. It is tedious to manually construct multi-blocks for large number of geometries on an individual basis to develop personalized FE models. Mesh-morphing method mitigates the aforementioned tediousness in meshing personalized geometries every time, but leads to element warping and loss of geometrical data. Such issues increase in magnitude when normative spine FE model is morphed to scoliosis-affected spinal geometry. The only way to bypass the issue of hex-mesh distortion or loss of geometry as a result of morphing is to rely on manually constructing the multi-blocks for scoliosis-affected spine geometry of each individual, which is time intensive. A method to semi-automate the construction of multi-blocks on the geometry of scoliosis vertebrae from the existing multi-blocks of normative vertebrae is demonstrated in this paper. High-quality hexahedral elements were generated on the scoliosis vertebrae from the morphed multi-blocks of normative vertebrae. Time taken was 3 months to construct the multi-blocks for normative spine and less than a day for scoliosis. Efforts taken to construct multi-blocks on personalized scoliosis spinal geometries are significantly reduced by morphing existing multi-blocks.

Predicting muscle fatigue: a response surface approximation based on proper generalized decomposition technique

A novel technique is proposed to predict force reduction in skeletal muscle due to fatigue under the influence of electrical stimulus parameters and muscle physiological characteristics. Twelve New Zealand white rabbits were divided in four groups ([Formula: see text]) to obtain the active force evolution of in vitro Extensor Digitorum Longus muscles for an hour of repeated contractions under different electrical stimulation patterns. Left and right muscles were tested, and a total of 24 samples were used to construct a response surface based in the proper generalized decomposition. After the response surface development, one additional rabbit was used to check the predictive potential of the technique. This multidimensional surface takes into account not only the decay of the maximum repeated peak force, but also the shape evolution of each contraction, muscle weight, electrical input signal and stimulation protocol. This new approach of the fatigue simulation challenge allows to predict, inside the multispace surface generated, the muscle response considering other stimulation patterns, different tissue weight, etc.

An applet to estimate the IOP-induced stress and strain within the optic nerve head

PURPOSE

The ability to predict the biomechanical response of the optic nerve head (ONH) to intraocular pressure (IOP) elevation holds great promise, yet remains elusive. The objective of this work was to introduce an approach to model ONH biomechanics that combines the ease of use and speed of analytical models with the flexibility and power of numerical models.

METHODS

Models representing a variety of ONHs were produced, and finite element (FE) techniques used to predict the stresses (forces) and strains (relative deformations) induced on each of the models by IOP elevations (up to 10 mm Hg). Multivariate regression was used to parameterize each biomechanical response as an analytical function. These functions were encoded into a Flash-based applet. Applet utility was demonstrated by investigating hypotheses concerning ONH biomechanics posited in the literature.

RESULTS

All responses were parameterized well by polynomials (R² values between 0.985 and 0.999), demonstrating the effectiveness of our fitting approach. Previously published univariate results were reproduced with the applet in seconds. A few minutes allowed for multivariate analysis, with which it was predicted that often, but not always, larger eyes experience higher levels of stress and strain than smaller ones, even at the same IOP.

CONCLUSIONS

An applet has been presented with which it is simple to make rapid estimates of IOP-related ONH biomechanics. The applet represents a step toward bringing the power of FE modeling beyond the specialized laboratory and can thus help develop more refined biomechanics-based hypotheses. The applet is available for use at www.ocularbiomechanics.com.

IOP-induced lamina cribrosa displacement and scleral canal expansion: an analysis of factor interactions using parameterized eye-specific models

PURPOSE

To study the anterior-posterior lamina cribrosa deformation (LCD) and the scleral canal expansion (SCE) produced by an increase in IOP and identify the main factors and interactions that determine these responses in the monkey.

METHODS

Eye-specific baseline models of the LC and sclera of both eyes of three normal monkeys were constructed. Morphing techniques were used to generate 888 models with controlled variations in LC thickness, position and modulus (stiffness), scleral thickness and modulus, and scleral canal size and eccentricity. Finite element modeling was used to simulate an increase in IOP from 10 to 15 mm Hg. A two-level, full-factorial experimental design was used to select factor combinations and to determine the sensitivity of LCD and SCE to the eight factors, independently and in interaction.

RESULTS

LCD was between 53.6 μm (posteriorly) and -12.9 μm (anteriorly), whereas SCE was between 0.5 and 15.2 μm (all expansions). LCD was most sensitive to laminar modulus and position (24% and 21% of the variance in LCD, respectively), whereas SCE was most sensitive to scleral modulus and thickness (46% and 36% of the variance in SCE, respectively). There were also strong interactions between factors (35% and 7% of the variance in LCD and SCE, respectively).

CONCLUSIONS

IOP-related LCD and SCE result from a complex combination of factors, including geometry and material properties of the LC and sclera. This work lays the foundation for interpreting the range of individual sensitivities to IOP and illustrates that predicting individual ONH response to IOP will require the measurement of multiple factors.

Evaluation of the generality and accuracy of a new mesh morphing procedure for the human femur

Various papers described mesh morphing techniques for computational biomechanics, but none of them provided a quantitative assessment of generality, robustness, automation, and accuracy in predicting strains. This study aims to quantitatively evaluate the performance of a novel mesh-morphing algorithm. A mesh-morphing algorithm based on radial-basis functions and on manual selection of corresponding landmarks on template and target was developed. The periosteal geometries of 100 femurs were derived from a computed tomography scan database and used to test the algorithm generality in producing finite element (FE) morphed meshes. A published benchmark, consisting of eight femurs for which in vitro strain measurements and standard FE model strain prediction accuracy were available, was used to assess the accuracy of morphed FE models in predicting strains. Relevant parameters were identified to test the algorithm robustness to operative conditions. Time and effort needed were evaluated to define the algorithm degree of automation. Morphing was successful for 95% of the specimens, with mesh quality indicators comparable to those of standard FE meshes. Accuracy of the morphed meshes in predicting strains was good (R(2)>0.9, RMSE%<10%) and not statistically different from the standard meshes (p-value=0.1083). The algorithm was robust to inter- and intra-operator variability, target geometry refinement (p-value>0.05) and partially to the number of landmark used. Producing a morphed mesh starting from the triangularized geometry of the specimen requires on average 10 min. The proposed method is general, robust, automated, and accurate enough to be used in bone FE modelling from diagnostic data, and prospectively in applications such as statistical shape modelling.