Mechanical testing of cancellous bone from the femoral head: Experimental errors due to off-axis measurements

Öhman, Caroline; Baleani, Massimiliano; Perilli, Egon; Dall’Ara, Enrico; Tassani, Simone; Baruffaldi, Fabio; Viceconti, Marco

doi:10.1016/j.jbiomech.2006.11.020

Cited by 95 publications

(62 citation statements)

References 32 publications

(45 reference statements)

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“…At that dimensional scale a number of studies have investigated the relationships between trabecular and cortical bone apparent mechanical properties and their volume fraction, mineral density and microarchitecture (Kaneko et al, 2003;Kopperdahl and Keaveny, 1998;Li et al, 2013;Nyman et al, 2009;Ohman et al, 2007;Wolfram et al, 2011), by means of mechanical testing and micro computed tomography (µCT) scanning. However, little is known about the local distribution of stress and strain of bone under certain loads, information necessary to study how bone structure and its local mechanical competence are related.…”

Section: Introductionmentioning

confidence: 99%

About the inevitable compromise between spatial resolution and accuracy of strain measurement for bone tissue: A 3D zero-strain study

Dall’Ara

Barber

Viceconti

2014

Journal of Biomechanics

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The accurate measurement of local strain is necessary to study bone mechanics and to validate micro computed tomography (µCT) based finite element (FE) models at the tissue scale. Digital volume correlation (DVC) has been used to provide a volumetric estimation of local strain in trabecular bone sample with a reasonable accuracy. However, nothing has been reported so far for µCT based analysis of cortical bone. The goal of this study was to evaluate accuracy and precision of a deformable registration method for prediction of local zero-strains in bovine cortical and trabecular bone samples.The accuracy and precision were analyzed by comparing scans virtually displaced, repeated scans without any repositioning of the sample in the scanner and repeated scans with repositioning of the samples.The analysis showed that both precision and accuracy errors decrease with increasing the size of the region analyzed, by following power laws. The main source of error was found to be the intrinsic noise of the images compared to the others investigated. The results, once extrapolated for larger regions of interest that are typically used in the literature, were in most cases better than the ones previously reported. For a nodal spacing equal to 50 voxels (498 µm), the accuracy and precision ranges were 425-692 µ and 202-394µ , respectively. In conclusion, it was shown that the proposed method can be used to study the local deformation of cortical and trabecular bone loaded beyond yield, if a sufficiently high nodal spacing is used.3

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Section: Introductionmentioning

confidence: 99%

About the inevitable compromise between spatial resolution and accuracy of strain measurement for bone tissue: A 3D zero-strain study

Dall’Ara

Barber

Viceconti

2014

Journal of Biomechanics

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“…Each specimen was cemented directly onto the testing machine. The free length of each specimen was 20 mm (Ohman et al, 2007;Perilli et al, 2008;Tassani et al, 2010). The same acquisition procedure was performed both before (pre-failure dataset) and after the mechanical testing (post-failure dataset).…”

Section: Methodsmentioning

confidence: 99%

“…Micro-CT allows visualizing and studying the trabecular framework performing non-destructive analysis. Following this procedure, correlation studies of the framework morphology with the mechanical behavior can be carried out (Ohman et al, 2007;Nazarian et al, 2009). The combination of the two techniques soon showed the importance of trabecular framework heterogeneity in the evaluation of the mechanical behavior of bone.…”

mentioning

confidence: 99%

Trabecular Fracture Zone Might Not Be the Higher Strain Region of the Trabecular Framework

et al. 2018

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Trabecular bone fracture is a traumatic and localized event studied worldwide in order to predict it. During the years, researchers focused over the mechanical characterization of the trabecular tissue to understand its mechanics. Several studies pointed out the very local nature of the trabecular failure, finally identifying the fracture zone with the aim to study it separately. The complexity of the three-dimensional trabecular framework and the local nature of the fracture event do not allow the direct evaluation of a single trabecula's behavior within its natural environment. For this reason, micro-Finite Element Modeling has been seen as the best way to investigate this biomechanical issue. Mechanical strain analysis is adopted in the literature for the identification of micro fracture using criteria based on principal strains. However, it was never verified if the fracture zone is actually the zone where principal strains are concentrated. Here, we show how the maximum strain of the tissue might not be directly correlated to the fracture. In the present work, a previously validated technique was used to identify the fracture zone of 10 trabecular specimen mechanically tested in compression and scanned in micro-CT before and after the mechanical test. Before-compression datasets were used to develop 10 micro-FE models were the same boundary conditions of the mechanical test were reproduced. Our results show how the known linear behavior of the trabecular framework might not be directly related to the development of the fracture suggesting other non-linear phenomenon, like buckling or microdamage, as actual cause of the traumatic event. This result might have several implications both in micro-modeling and in clinical applications for the study of fracture related pathology, like osteoporosis.

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“…It also allows the prediction of an outcome (output) of a system based on values of a set of predictor variables (inputs), which can be measured in the experiments or represent clinical data. Several computational modelling studies of mechanical properties of the trabecular bone 28,29 have shown that a theoretical mathematical approach can be applied to determine the effects of scaffold micro-architecture on new bone formation. This comprehensive model allowed successful theoretical simulation of bone regeneration and the scaffold mass loss over time.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, experimentally derived specific mechanical properties, such as compressive strength, hardness and Young's modulus of trabecular bone, were used. 28,29 These studies provided valuable and experimentally validated conclusions regarding compressive properties of the trabecular bone; however, the complexity of the experiments failed to consider the mechanical properties in conjunction with the essential structural and mechanobiological parameters of the trabecular bone behaviour for its use in hard tissue engineering. Therefore, the current state of research on trabecular bone modelling demands a more comprehensive model that could integrate in a single system with various mechanical, structural and biological aspects of the trabecular bone and yet be based on the actual experimental/clinical data.…”

Section: Introductionmentioning

confidence: 99%

Neural networks for analysis of trabecular bone in osteoarthritis

Khovanova

Shaikhina

Mallick

2015

Bioinspired, Biomimetic and Nanobiomaterials

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This study investigated the correlation of age in male and female specimens with physico-mechanical properties of trabecular bone including compressive strength, bone volume fraction, structural model index, trabecular thickness factor, level of inter-connectivity and pore morphology. An artificial neural network was designed to analyse 35 available samples in order to account for complex inter-dependencies of the key parameters in multi-dimensional space. Trained by using Levenberg-Marquardt back propagation algorithm, the network achieved regression factor of 0·96 by optimisation and showed that age correlates strongly with the physical properties of the bone affected by severe osteoarthritis. In addition, the compressive strength was found to be the most important factor for predicting the bone aging. Within the limitations of the input data set, the model developed provides a reliable predictive tool to tissue engineering applications. IntroductionBone is a natural biocomposite comprising hierarchical cortical and trabecular (cancellous) structures. The skeletal structurefunction relationship of trabecular tissue in vivo is complex 1 since the mechano-biological properties are intrinsically dependent on the physical and geometrical parameters of bone, 2 for example, three-dimensional (3D) trabecular architecture, 3 pore size, compressive strength, 4 yield strain and modulus, 5,6 strain energy density and bone remodelling. The task of optimising or predicting these properties especially for trabecular bone becomes even more onerous due to the fact that mechanical properties of bone differ according to their anatomical location.7 Tissue engineers are thus often faced with problems of selecting the most successful strategy for both the design and fabrication of synthetic scaffold for the treatment of patients suffering from degenerative orthopaedic diseases triggered by osteoarthritis, osteoporosis, trauma, injury and metastatic cancer occurring in specific age group and gender. 8,9 Computational techniques such as finite-element analysis (FEA) and other mathematical procedures have been used for clinical data in hip fractures with limited success.10-12 Phenomenological (data-driven) models, based on the experimental or clinical data, are known to have poor accuracy and restricted by the size of available data sets.13 Resulting statistical models are generally very sensitive to the inaccurate data and outliers, as well as the level of ambiguity allowed by the fuzzy logic and soft algorithms. This necessitates generation of large volume and high-quality data, and in tissue engineering, these requirements are highly impractical and often unrealistic. To resolve this dilemma and make statistical datadriven models applicable, it is desirable to design a model based on the principles of machine learning, which enables the ability of such model to learn from the additional data and to dynamically adapt to new inputs. [14][15][16][17]

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Mechanical testing of cancellous bone from the femoral head: Experimental errors due to off-axis measurements

Cited by 95 publications

References 32 publications

About the inevitable compromise between spatial resolution and accuracy of strain measurement for bone tissue: A 3D zero-strain study

About the inevitable compromise between spatial resolution and accuracy of strain measurement for bone tissue: A 3D zero-strain study

Trabecular Fracture Zone Might Not Be the Higher Strain Region of the Trabecular Framework

Neural networks for analysis of trabecular bone in osteoarthritis

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