Measurement of the acoustic reflectance function based on Lamb wave using high f-number transducers

Ultrasonic reflectivity using a V(z) technique is a powerful characterization method in acoustic microscopy to measure the elastic properties of materials. Conventional techniques generally use a low f-number with high frequency; however, to measure the reflectance function of the highly attenuative material, a low frequency is essential. In this study, the transducer-pair method based on Lamb waves is used to measure the reflectance function of a highly attenuative material. The results demonstrate the feasibility of the proposed method using a commercial ultrasound transducer with high f-number.

Effect of hydrogen coverage on elastic and optical properties of silicene: a first-principle study

The structural, electronic, and optical properties of hydrogenated silicene have been investigated using first-principles DFT calculations. Compared to pristine silicene, hydrogenated silicene exhibits high stability, reduced anisotropy, and less birefringence. Hydrogenated silicene shows a constant refractive index in the visible region, increasing exponentially in silicene. The elastic and optical parameters such as Young's modulus (Y), Poisson's ratio (ν), bulk modulus (B), shear modulus (G), dielectric constant ε(0), refractive index n(0), conductivity threshold (E_th), birefringence Δn(0), and plasmon energy (ħω_p) were calculated for the first time for different hydrogen coverage on silicene, which is crucial in the applications of linear and non-linear optoelectronic devices. The estimated parameters agree well with the available experimental and reported values.

Effect of fabric on the accuracy of computed tomography-based finite element analyses of the vertebra

Quantitative computed tomography (QCT)-based finite element (FE) models of the vertebra are widely used in studying spine biomechanics and mechanobiology, but their accuracy has not been fully established. Although the models typically assign material properties based only on local bone mineral density (BMD), the mechanical behavior of trabecular bone also depends on fabric. The goal of this study was to determine the effect of incorporating measurements of fabric on the accuracy of FE predictions of vertebral deformation. Accuracy was assessed by using displacement fields measured via digital volume correlation-applied to time-lapse microcomputed tomography (μCT)-as the gold standard. Two QCT-based FE models were generated from human L1 vertebrae (n = 11): the entire vertebral body and a cuboid-shaped portion of the trabecular centrum [dimensions: (20-30) × (15-20) × (15-20) mm3]. For axial compression boundary conditions, there was no difference (p = 0.40) in the accuracy of the FE-computed displacements for models using material properties based on local values of BMD versus those using material properties based on local values of fabric and volume fraction. However, when using BMD-based material properties, errors were higher for the vertebral-body models (8.4-50.1%) than cuboid models (1.5-19.6%), suggesting that these properties are inaccurate in the peripheral regions of the centrum. Errors also increased when assuming that the cuboid region experienced uniaxial loading during axial compression of the vertebra. These findings indicate that a BMD-based constitutive model is not sufficient for the peripheral region of the vertebral body when seeking accurate QCT-based FE modeling of the vertebra.

A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images

Biological collagenous tissues comprised of networks of collagen fibers are suitable for a broad spectrum of medical applications owing to their attractive mechanical properties. In this study, we developed a noninvasive approach to estimate collagenous tissue elastic properties directly from microscopy images using Machine Learning (ML) techniques. Glutaraldehyde-treated bovine pericardium (GLBP) tissue, widely used in the fabrication of bioprosthetic heart valves and vascular patches, was chosen to develop a representative application. A Deep Learning model was designed and trained to process second harmonic generation (SHG) images of collagen networks in GLBP tissue samples, and directly predict the tissue elastic mechanical properties. The trained model is capable of identifying the overall tissue stiffness with a classification accuracy of 84%, and predicting the nonlinear anisotropic stress-strain curves with average regression errors of 0.021 and 0.031. Thus, this study demonstrates the feasibility and great potential of using the Deep Learning approach for fast and noninvasive assessment of collagenous tissue elastic properties from microstructural images.

STATEMENT OF SIGNIFICANCE

In this study, we developed, to our best knowledge, the first Deep Learning-based approach to estimate the elastic properties of collagenous tissues directly from noninvasive second harmonic generation images. The success of this study holds promise for the use of Machine Learning techniques to noninvasively and efficiently estimate the mechanical properties of many structure-based biological materials, and it also enables many potential applications such as serving as a quality control tool to select tissue for the manufacturing of medical devices (e.g. bioprosthetic heart valves).