Mechanical measurement of the human cerebellum under compressive loading

Applied use of biomechanical measurements from human tissues for the development of medical skills trainers: a scoping review

OBJECTIVE

The objective of this review was to identify quantitative biomechanical measurements of human tissues, the methods for obtaining these measurements, and the primary motivations for conducting biomechanical research.

INTRODUCTION

Medical skills trainers are a safe and useful tool for clinicians to use when learning or practicing medical procedures. The haptic fidelity of these devices is often poor, which may be because the synthetic materials chosen for these devices do not have the same mechanical properties as human tissues. This review investigates a heterogeneous body of literature to identify which biomechanical properties are available for human tissues, the methods for obtaining these values, and the primary motivations behind conducting biomechanical tests.

INCLUSION CRITERIA

Studies containing quantitative measurements of the biomechanical properties of human tissues were included. Studies that primarily focused on dynamic and fluid mechanical properties were excluded. Additionally, studies only containing animal, in silico , or synthetic materials were excluded from this review.

METHODS

This scoping review followed the JBI methodology for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Sources of evidence were extracted from CINAHL (EBSCO), IEEE Xplore, MEDLINE (PubMed), Scopus, and engineering conference proceedings. The search was limited to the English language. Two independent reviewers screened titles and abstracts as well as full-text reviews. Any conflicts that arose during screening and full-text review were mediated by a third reviewer. Data extraction was conducted by 2 independent reviewers and discrepancies were mediated through discussion. The results are presented in tabular, figure, and narrative formats.

RESULTS

Data were extracted from a total of 186 full-text publications. All of the studies, except for 1, were experimental. Included studies came from 33 countries, with the majority coming from the United States. Ex vivo methods were the predominant approach for extracting human tissue samples, and the most commonly studied tissue type was musculoskeletal. In this study, nearly 200 unique biomechanical values were reported, and the most commonly reported value was Young's (elastic) modulus. The most common type of mechanical test performed was tensile testing, and the most common reason for testing human tissues was to characterize biomechanical properties. Although the number of published studies on biomechanical properties of human tissues has increased over the past 20 years, there are many gaps in the literature. Of the 186 included studies, only 7 used human tissues for the design or validation of medical skills training devices. Furthermore, in studies where biomechanical values for human tissues have been obtained, a lack of standardization in engineering assumptions, methodologies, and tissue preparation may implicate the usefulness of these values.

CONCLUSIONS

This review is the first of its kind to give a broad overview of the biomechanics of human tissues in the published literature. With respect to high-fidelity haptics, there is a large gap in the published literature. Even in instances where biomechanical values are available, comparing or using these values is difficult. This is likely due to the lack of standardization in engineering assumptions, testing methodology, and reporting of the results. It is recommended that journals and experts in engineering fields conduct further research to investigate the feasibility of implementing reporting standards.

REVIEW REGISTRATION

Open Science Framework https://osf.io/fgb34.

Inverse identification of region-specific hyperelastic material parameters for human brain tissue

The identification of material parameters accurately describing the region-dependent mechanical behavior of human brain tissue is crucial for computational models used to assist, e.g., the development of safety equipment like helmets or the planning and execution of brain surgery. While the division of the human brain into different anatomical regions is well established, knowledge about regions with distinct mechanical properties remains limited. Here, we establish an inverse parameter identification scheme using a hyperelastic Ogden model and experimental data from multi-modal testing of tissue from 19 anatomical human brain regions to identify mechanically distinct regions and provide the corresponding material parameters. We assign the 19 anatomical regions to nine governing regions based on similar parameters and microstructures. Statistical analyses confirm differences between the regions and indicate that at least the corpus callosum and the corona radiata should be assigned different material parameters in computational models of the human brain. We provide a total of four parameter sets based on the two initial Poisson's ratios of 0.45 and 0.49 as well as the pre- and unconditioned experimental responses, respectively. Our results highlight the close interrelation between the Poisson's ratio and the remaining model parameters. The identified parameters will contribute to more precise computational models enabling spatially resolved predictions of the stress and strain states in human brains under complex mechanical loading conditions.

Tissue-Scale Biomechanical Testing of Brain Tissue for the Calibration of Nonlinear Material Models

Brain tissue is one of the most complex and softest tissues in the human body. Due to its ultrasoft and biphasic nature, it is difficult to control the deformation state during biomechanical testing and to quantify the highly nonlinear, time-dependent tissue response. In numerous experimental studies that have investigated the mechanical properties of brain tissue over the last decades, stiffness values have varied significantly. One reason for the observed discrepancies is the lack of standardized testing protocols and corresponding data analyses. The tissue properties have been tested on different length and time scales depending on the testing technique, and the corresponding data have been analyzed based on simplifying assumptions. In this review, we highlight the advantage of using nonlinear continuum mechanics based modeling and finite element simulations to carefully design experimental setups and protocols as well as to comprehensively analyze the corresponding experimental data. We review testing techniques and protocols that have been used to calibrate material model parameters and discuss artifacts that might falsify the measured properties. The aim of this work is to provide standardized procedures to reliably quantify the mechanical properties of brain tissue and to more accurately calibrate appropriate constitutive models for computational simulations of brain development, injury and disease. Computational models can not only be used to predictively understand brain tissue behavior, but can also serve as valuable tools to assist diagnosis and treatment of diseases or to plan neurosurgical procedures. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

A Minireview on Brain Models Simulating Geometrical, Physical, and Biochemical Properties of the Human Brain

There is a growing body of evidences that brain surrogates will be of great interest for researchers and physicians in the medical field. They are currently mainly used for education and training purposes or to verify the appropriate functionality of medical devices. Depending on the purpose, a variety of materials have been used with specific and accurate mechanical and biophysical properties, More recently they have been used to assess the biocompatibility of implantable devices, but they are still not validated to study the migration of leaching components from devices. This minireview shows the large diversity of approaches and uses of brain phantoms, which converge punctually. All these phantoms are complementary to numeric models, which benefit, reciprocally, of their respective advances. It also suggests avenues of research for the analysis of leaching components from implantable devices.