Analysis of friction in quantitative micro-elastography.

Quantitative micro-elastography (QME) is a compression-based optical coherence elastography technique capable of measuring the mechanical properties of tissue on the micro-scale. As QME requires contact between the imaging window and the sample, the presence of friction affects the accuracy of the estimated elasticity. In previous implementations, a lubricant was applied at the contact surfaces, which was assumed to result in negligible friction. However, recently, errors in the estimation of elasticity caused by friction have been reported. This effect has yet to be characterized and is, therefore, not well understood. In this work, we present a systematic analysis of friction in QME using silicone phantoms. We demonstrate that friction, and, therefore, the elasticity accuracy, is influenced by several experimental factors, including the viscosity of the lubricant, the mechanical contrast between the compliant layer and the sample, and the time after the application of a compressive strain. Elasticity errors over an order of magnitude were observed in the absence of appropriate lubrication when compared to uniaxial compression testing. Using an optimized lubrication protocol, we demonstrate accurate elasticity estimation (<10% error) for nonlinear elastic samples with Young's moduli ranging from 3 kPa to 130 kPa. Finally, using a structured phantom, we demonstrate that friction can significantly reduce mechanical contrast in QME. We believe that the framework established in this study will facilitate more robust elasticity estimations in QME, as well as being readily adapted to understand the effects of friction in other contact elastography techniques.

3D Volumetric Mechanosensation of MCF7 Breast Cancer Spheroids in a Linear Stiffness Gradient GelAGE.

The tumor microenvironment presents spatiotemporal shifts in biomechanical properties with cancer progression. Hydrogel biomaterials like GelAGE offer the stiffness tuneability to recapitulate dynamic changes in tumor tissues by altering photo-energy exposures. Here, a tuneable hydrogel with spatiotemporal control of stiffness and mesh-network is developed. The volume of MCF7 spheroids encapsulated in a linear stiffness gradient demonstrates an inverse relationship with stiffness (p < 0.0001). As spheroids are exposed to increased crosslinking (stiffer) and greater mechanical confinement, spheroid stiffness increases. Protein expression (TRPV4, ß1 integrin, E-cadherin, and F-actin) decreases with increasing stiffness while showing strong correlations to spheroid volume (r2  > 0.9). To further investigate the role of volume, MCF7 spheroids are grown in a soft matrix for 5 days prior to a second polymerisation which presents a stiffness gradient to equally expanded spheroids. Despite being exposed to variable stiffness, these spheroids show even protein expression, confirming volume as a key regulator. Overall, this work showcases the versatility of GelAGE and demonstrates volume expansion as a key regulator of 3D mechanosensation in MCF7 breast cancer spheroids. This platform has the potential to further investigation into the role of stiffness and dimensionality in 3D spheroid culture for other types of cancers and diseases.

Biomechanical assessment of chronic liver injury using quantitative micro-elastography.

Hepatocellular carcinoma is one of the most lethal cancers worldwide, causing almost 700,000 deaths annually. It mainly arises from cirrhosis, which, in turn, results from chronic injury to liver cells and corresponding fibrotic changes. Although it is known that chronic liver injury increases the elasticity of liver tissue, the role of increased elasticity of the microenvironment as a possible hepatocarcinogen is yet to be investigated. One reason for this is the paucity of imaging techniques capable of mapping the micro-scale elasticity variation in liver and correlating that with cancerous mechanisms on the cellular scale. The clinical techniques of ultrasound elastography and magnetic resonance elastography typically do not provide micro-scale resolution, while atomic force microscopy can only assess the elasticity of a limited number of cells. We propose quantitative micro-elastography (QME) for mapping the micro-scale elasticity of liver tissue into images known as micro-elastograms, and therefore, as a technique capable of correlating the micro-environment elasticity of tissue with cellular scale cancerous mechanisms in liver. We performed QME on 13 freshly excised healthy and diseased mouse livers and present micro-elastograms, together with co-registered histology, in four representative cases. Our results indicate a significant increase in the mean (×6.3) and standard deviation (×6.0) of elasticity caused by chronic liver injury and demonstrate that the onset and progression of pathological features such as fibrosis, hepatocyte damage, and immune cell infiltration correlate with localized variations in micro-elastograms.

Subcellular mechano-microscopy: high resolution three-dimensional elasticity mapping using optical coherence microscopy.

The importance of cellular-scale mechanical properties is well-established, yet it is challenging to map subcellular elasticity in three dimensions. We present subcellular mechano-microscopy, an optical coherence microscopy (OCM)-based variant of three-dimensional (3-D) compression optical coherence elastography (OCE) that provides an elasticity system resolution of 5 × 5 × 5 µm: a 7-fold improvement in system resolution over previous OCE studies of cells. The improved resolution is achieved through a ∼5-fold improvement in optical resolution, refinement of the strain estimation algorithm, and demonstration that mechanical deformation of subcellular features provides feature resolution far greater than that demonstrated previously on larger features with diameter >250 µm. We use mechano-microscopy to image adipose-derived stem cells encapsulated in gelatin methacryloyl. We compare our results with compression OCE and demonstrate that mechano-microscopy can provide contrast from subcellular features not visible using OCE.

Three-dimensional mechanical characterization of murine skeletal muscle using quantitative micro-elastography.

Skeletal muscle function is governed by both the mechanical and structural properties of its constituent tissues, which are both modified by disease. Characterizing the mechanical properties of skeletal muscle tissue at an intermediate scale, i.e., between that of cells and organs, can provide insight into diseases such as muscular dystrophies. In this study, we use quantitative micro-elastography (QME) to characterize the micro-scale elasticity of ex vivo murine skeletal muscle in three-dimensions in whole muscles. To address the challenge of achieving high QME image quality with samples featuring uneven surfaces and geometry, we encapsulate the muscles in transparent hydrogels with flat surfaces. Using this method, we study aging and disease in quadriceps tissue by comparing normal wild-type (C57BL/6J) mice with dysferlin-deficient BLAJ mice, a model for the muscular dystrophy dysferlinopathy, at 3, 10, and 24 months of age (sample size of three per group). We observe a 77% decrease in elasticity at 24 months in dysferlin-deficient quadriceps compared to wild-type quadriceps.

Analysis of sensitivity in quantitative micro-elastography.

Quantitative micro-elastography (QME), a variant of compression optical coherence elastography (OCE), is a technique to image tissue elasticity on the microscale. QME has been proposed for a range of applications, most notably tumor margin assessment in breast-conserving surgery. However, QME sensitivity, a key imaging metric, has yet to be systematically analyzed. Consequently, it is difficult to optimize imaging performance and to assess the potential of QME in new application areas. To address this, we present a framework for analyzing sensitivity that incorporates the three main steps in QME image formation: mechanical deformation, its detection using optical coherence tomography (OCT), and signal processing used to estimate elasticity. Firstly, we present an analytical model of QME sensitivity, validated by experimental data, and demonstrate that sub-kPa elasticity sensitivity can be achieved in QME. Using silicone phantoms, we demonstrate that sensitivity is dependent on friction, OCT focus depth, and averaging methods in signal processing. For the first time, we show that whilst lubrication of layer improves accuracy by reducing surface friction, it reduces sensitivity due to the time-dependent effect of lubricant exudation from the layer boundaries resulting in increased friction. Furthermore, we demonstrate how signal processing in QME provides a trade-off between sensitivity and resolution that can be used to optimize imaging performance. We believe that our framework to analyze sensitivity can help to sustain the development of QME and, also, that it can be readily adapted to other OCE techniques.

Strain and elasticity imaging in compression optical coherence elastography: The two-decade perspective and recent advances.

Quantitative mapping of deformation and elasticity in optical coherence tomography has attracted much attention of researchers during the last two decades. However, despite intense effort it took ~15 years to demonstrate optical coherence elastography (OCE) as a practically useful technique. Similarly to medical ultrasound, where elastography was first realized using the quasi-static compression principle and later shear-wave-based systems were developed, in OCE these two approaches also developed in parallel. However, although the compression OCE (C-OCE) was proposed historically earlier in the seminal paper by J. Schmitt in 1998, breakthroughs in quantitative mapping of genuine local strains and the Young's modulus in C-OCE have been reported only recently and have not yet obtained sufficient attention in reviews. In this overview, we focus on underlying principles of C-OCE; discuss various practical challenges in its realization and present examples of biomedical applications of C-OCE. The figure demonstrates OCE-visualization of complex transient strains in a corneal sample heated by an infrared laser beam.

Three-dimensional imaging of cell and extracellular matrix elasticity using quantitative micro-elastography.

Recent studies in mechanobiology have revealed the importance of cellular and extracellular mechanical properties in regulating cellular function in normal and disease states. Although it is established that cells should be investigated in a three-dimensional (3-D) environment, most techniques available to study mechanical properties on the microscopic scale are unable to do so. In this study, for the first time, we present volumetric images of cellular and extracellular elasticity in 3-D biomaterials using quantitative micro-elastography (QME). We achieve this by developing a novel strain estimation algorithm based on 3-D linear regression to improve QME system resolution. We show that QME can reveal elevated elasticity surrounding human adipose-derived stem cells (ASCs) embedded in soft hydrogels. We observe, for the first time in 3-D, further elevation of extracellular elasticity around ASCs with overexpressed TAZ; a mechanosensitive transcription factor which regulates cell volume. Our results demonstrate that QME has the potential to study the effects of extracellular mechanical properties on cellular functions in a 3-D micro-environment.

Polarization-sensitive laser feedback interferometry for specular reflection removal.

Specular reflection from the surface of targets or prepared specimens represents a significant problem in optical microscopy and related optical imaging techniques as usually the surface reflection does not contribute to the desired signal. Solutions exist for many of these imaging techniques; however, remedial techniques for imaging based on laser feedback interferometry (LFI) are absent. We propose a reflection cancellation technique based on crossed-polarization filtering that is tailored for a typical LFI configuration. The technique is validated with three experimental designs, and a significant improvement of about 40 dB in the ratio of the diffuse and specular LFI signal is observed. Applications of this principle extend from specular reflection removal to characterization of target materials in industrial to biomedical domains.

Confocal laser feedback tomography for skin cancer detection.

Tomographic imaging of soft tissue such as skin has a potential role in cancer detection. The penetration of infrared wavelengths makes a confocal approach based on laser feedback interferometry feasible. We present a compact system using a semiconductor laser as both transmitter and receiver. Numerical and physical models based on the known optical properties of keratinocyte cancers were developed. We validated the technique on three phantoms containing macro-structural changes in optical properties. Experimental results were in agreement with numerical simulations and structural changes were evident which would permit discrimination of healthy tissue and tumour. Furthermore, cancer type discrimination was also able to be visualized using this imaging technique.

Concurrent Reflectance Confocal Microscopy and Laser Doppler Flowmetry to Improve Skin Cancer Imaging: A Monte Carlo Model and Experimental Validation.

Optical interrogation of suspicious skin lesions is standard care in the management of skin cancer worldwide. Morphological and functional markers of malignancy are often combined to improve expert human diagnostic power. We propose the evaluation of the combination of two independent optical biomarkers of skin tumours concurrently. The morphological modality of reflectance confocal microscopy (RCM) is combined with the functional modality of laser Doppler flowmetry, which is capable of quantifying tissue perfusion. To realize the idea, we propose laser feedback interferometry as an implementation of RCM, which is able to detect the Doppler signal in addition to the confocal reflectance signal. Based on the proposed technique, we study numerical models of skin tissue incorporating two optical biomarkers of malignancy: (i) abnormal red blood cell velocities and concentrations and (ii) anomalous optical properties manifested through tissue confocal reflectance, using Monte Carlo simulation. We also conduct a laboratory experiment on a microfluidic channel containing a dynamic turbid medium, to validate the efficacy of the technique. We quantify the performance of the technique by examining a signal to background ratio (SBR) in both the numerical and experimental models, and it is shown that both simulated and experimental SBRs improve consistently using this technique. This work indicates the feasibility of an optical instrument, which may have a role in enhanced imaging of skin malignancies.

Effect of the optical system on the Doppler spectrum in laser-feedback interferometry.

We present a comprehensive analysis of factors influencing the morphology of the Doppler spectrum obtained from a laser-feedback interferometer. We explore the effect of optical system parameters on three spectral characteristics: central Doppler frequency, broadening, and signal-to-noise ratio. We perform four sets of experiments and replicate the results using a Monte Carlo simulation calibrated to the backscattering profile of the target. We classify the optical system parameters as having a strong or weak influence on the Doppler spectrum. The calibrated Monte Carlo approach accurately reproduces experimental results, and allows one to investigate the detailed contribution of system parameters to the Doppler spectrum, which are difficult to isolate in experiment.

Self-mixing sensing system based on uncooled vertical-cavity surface-emitting laser array: linking multichannel operation and enhanced performance.

We compare the performance of a self-mixing (SM) sensing system based on an uncooled monolithic array of 24×1 vertical-cavity surface-emitting lasers (VCSELs) in two modes of operation: single active channel and the concurrent multichannel operation. We find that the signal-to-noise ratio of individual SM sensors in a VCSEL array is markedly improved by multichannel operation, as a consequence of the increased operational temperature of the sensors. The performance improvement can be further increased by manufacturing VCSEL arrays with smaller pitch. This has the potential to produce an imaging system with high spatial and temporal resolutions that can be operated without temperature stabilization.

Optimum design of a hybrid erbium-doped fiber amplifier/fiber Raman amplifier using particle swarm optimization.

We propose and optimize a hybrid erbium-doped fiber amplifier/fiber Raman amplifier (EDFA/FRA). A large number of parameters of a wide-band hybrid amplifier consisting of an erbium-doped fiber amplifier (EDFA) and a fiber Raman amplifier (FRA) have been optimized using an effective and fast global optimization method called particle swarm optimization. Two types of hybrid EDFA/FRA with six- and 10-pumped FRAs have been optimized. A large number of variables affect the hybrid EDFA/FRA performance, thus we need a global optimization method to be able to deal with these variables. Particle swarm optimization helps us to find optimum parameters of a hybrid EDFA/FRA and reduce the gain spectrum variations to 2.91 and 2.03 dB for the six and 10 pumped FRAs, respectively. The optimum design supports the amplification of 60 signal channels in the wavelength range of 1529.2-1627.1 nm for a wavelength-division multiplexing system.

Design of a flat-gain multipumped distributed fiber Raman amplifier by particle swarm optimization.

The pumping scheme of multipumped distributed fiber Raman amplifiers is optimized by a powerful method called particle swarm optimization. By use of particle swarm optimization, we optimize both pump powers and frequencies of multipumped Raman amplifiers with a high number of pumps. Particle swarm optimization is a fast and effective method, and it surpasses other optimization methods, such as the genetic algorithm, for optimizing fiber amplifiers. It is shown that the computational efficiency of particle swarm optimization is significantly better than that of the genetic algorithm, reducing the time of computation to one third, and its implementation is more straightforward. A gain bandwidth of 92.1 nm and a gain variation of 0.49 dB in the range of 1524.5-1616.6 nm are obtained by this method, using ten backward pumps in a 60-km-long amplifier. The gain variation reduction is due to the inclusion of pump frequencies in the optimization process.