Development and experimental validation of a dynamic numerical model for human articular cartilage.

The purpose of this study was to create a preliminary set of experimentally validated Finite Element Analysis (FEA) models, in order to predict the dynamic mechanical behaviour of human articular cartilage (AC). Current models consider static loading with limited independent experimental validation, while the models for this study assess dynamic loading of AC, with direct comparison and validation to physical testing. Three different FEA models of AC were constructed, which considered both linear elastic and hyperelastic models; Neo-Hookean and Ogden. Models were validated using the data collected from compression testing of human femoral heads across 0-1.7 MPa (quasi-static tests and dynamic mechanical analysis). The linear elastic model was inadequate, with a 10-fold over prediction of the displacement dynamic amplitude. The Neo-Hookean model accurately predicted the dynamic amplitude but failed to predict the initial compression of the cartilage, with a 10 times overprediction. The Ogden model provided the best results, with both the initial compression lying within one standard deviation of that observed in the validation data set, and the dynamic amplitude of the same order of magnitude. In conclusion, this study has found that the fast dynamic response of human AC is best represented by a third order Ogden model.

Applications of compressive sensing in spatial frequency domain imaging.

SIGNIFICANCE: Spatial frequency domain imaging (SFDI) is an imaging modality that projects spatially modulated light patterns to determine optical property maps for absorption and reduced scattering of biological tissue via a pixel-by-pixel data acquisition and analysis procedure. Compressive sensing (CS) is a signal processing methodology which aims to reproduce the original signal with a reduced number of measurements, addressing the pixel-wise nature of SFDI. These methodologies have been combined for complex heterogenous data in both the image detection and data analysis stage in a compressive sensing SFDI (cs-SFDI) approach, showing reduction in both the data acquisition and overall computational time. AIM: Application of CS in SFDI data acquisition and image reconstruction significantly improves data collection and image recovery time without loss of quantitative accuracy. APPROACH: cs-SFDI has been applied to an increased heterogenic sample from the AppSFDI data set (back of the hand), highlighting the increased number of CS measurements required as compared to simple phantoms to accurately obtain optical property maps. A novel application of CS to the parameter recovery stage of image analysis has also been developed and validated. RESULTS: Dimensionality reduction has been demonstrated using the increased heterogenic sample at both the acquisition and analysis stages. A data reduction of 30% for the cs-SFDI and up to 80% for the parameter recover was achieved as compared to traditional SFDI, while maintaining an error of <10 % for the recovered optical property maps. CONCLUSION: The application of data reduction through CS demonstrates additional capabilities for multi- and hyperspectral SFDI, providing advanced optical and physiological property maps.

Machine learning utilising spectral derivative data improves cellular health classification through hyperspectral infra-red spectroscopy.

The objective differentiation of facets of cellular metabolism is important for several clinical applications, including accurate definition of tumour boundaries and targeted wound debridement. To this end, spectral biomarkers to differentiate live and necrotic/apoptotic cells have been defined using in vitro methods. The delineation of different cellular states using spectroscopic methods is difficult due to the complex nature of these biological processes. Sophisticated, objective classification methods will therefore be important for such differentiation. In this study, spectral data from healthy/traumatised cell samples using hyperspectral imaging between 2500-3500 nm were collected using a portable prototype device. Machine learning algorithms, in the form of clustering, have been performed on a variety of pre-processing data types including 'raw' unprocessed, smoothed resampling, background subtracted and spectral derivative. The resulting clusters were utilised as a diagnostic tool for the assessment of cellular health and quantified using both sensitivity and specificity to compare the different analysis methods. The raw data exhibited differences for one of the three different trauma types applied, although unable to accurately cluster all the traumatised samples due to signal contamination from the chemical insult. The background subtracted and smoothed data sets reduced the accuracy further, due to the apparent removal of key spectral features which exhibit cellular health. However, the spectral derivative data-types significantly improved the accuracy of clustering compared to other data types, with both sensitivity and specificity for the background subtracted data set being >94% highlighting its utility to account for unknown signal contamination while maintaining important cellular spectral features.

Dynamic viscoelastic characterisation of human osteochondral tissue: understanding the effect of the cartilage-bone interface.

BACKGROUND: Despite it being known that subchondral bone affects the viscoelasticity of cartilage, there has been little research into the mechanical properties of osteochondral tissue as a whole system. This study aims to unearth new knowledge concerning the dynamic behaviour of human subchondral bone and how energy is transferred through the cartilage-bone interface. METHODS: Dynamic mechanical analysis was used to determine the frequency-dependent (1-90 Hz) viscoelastic properties of the osteochondral unit (cartilage-bone system) as well as isolated cartilage and bone specimens extracted from human femoral heads obtained from patients undergoing total hip replacement surgery, with a mean age of 78 years (N = 5, n = 22). Bone mineral density (BMD) was also determined for samples using micro-computed tomography as a marker of tissue health. RESULTS: Cartilage storage and loss moduli along with bone storage modulus were found to increase logarithmically (p < 0.05) with frequency. The mean cartilage storage modulus was 34.4 ± 3.35 MPa and loss modulus was 6.17 ± 0.48 MPa (mean ± standard deviation). In contrast, bone loss modulus decreased logarithmically between 1 and 90 Hz (p < 0.05). The storage stiffness of the cartilage-bone-core was found to be frequency-dependent with a mean value of 1016 ± 54.0 N.mm- 1, while the loss stiffness was determined to be frequency-independent at 78.84 ± 2.48 N.mm- 1. Notably, a statistically significant (p < 0.05) linear correlation was found between the total energy dissipated from the isolated cartilage specimens, and the BMD of the isolated bone specimens at all frequencies except at 90 Hz (p = 0.09). CONCLUSIONS: The viscoelastic properties of the cartilage-bone core were significantly different to the tissues in isolation (p < 0.05). Results from this study demonstrate that the functionality of these tissues arises because they operate as a unit. This is evidenced through the link between cartilage energy dissipated and bone BMD. The results may provide insights into the functionality of the osteochondral unit, which may offer further understanding of disease progression, such as osteoarthritis (OA). Furthermore, the results emphasise the importance of studying human tissue, as bovine models do not always display the same trends.