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Compression optical coherence elastography forms images based on the mechanical properties of tissue by mapping the local strain in response to a compressive load. Strain is described by a second-order tensor, comprising six independent components. The majority of compression elastography methods, however, measure and form their analyses from a single axial component, which relies on the assumption that the stress in tissue is uniform and uniaxial. However, in general, tissues are complex and heterogeneous, and rarely comply with this assumption. This can lead to inaccuracies and misinterpretation of image contrast in compression optical coherence elastography. Here, we image the full strain tensor and demonstrate its utility in unambiguously characterizing deformation in structured phantoms and ex vivo tissues. We derive additional parameters from the strain tensor, and map the local compressibility, anisotropy of deformation, and total equivalent strain. Such analysis is enabled by an efficient non-iterative approach to measuring the three-dimensional displacement field via a closed-form solution to a collection of the amplitude of complex correlation coefficients across multiple digitally shifted images. Strain tensor imaging is likely to lead to more accurate estimation of tissue mechanical properties, improving the utility of compression optical coherence elastography in clinical and biological applications.

Quantitative elasticity imaging seeks to retrieve spatial maps of elastic moduli of tissue. Unlike strain, which is commonly imaged in compression elastography, elastic moduli are intrinsic properties of tissue, and therefore, this approach reconstructs images that are largely operator and system independent, enabling objective, longitudinal, and multisite diagnoses. Recently, novel quantitative elasticity imaging approaches to compression elastography have been developed. These methods use a calibration layer with known mechanical properties to sense the stress at the tissue surface, which combined with strain, is used to estimate the tissue's elastic moduli by assuming homogeneity in the stress field. However, this assumption is violated in mechanically heterogeneous samples. We present a more general approach to quantitative elasticity imaging that overcomes this limitation through an efficient iterative solution of the inverse elasticity problem using adjoint elasticity equations. We present solutions for linear elastic, isotropic, and incompressible solids; however, this method can be employed for more complex mechanical models. We retrieve the spatial distribution of shear modulus for a tissue-simulating phantom and a tissue sample. This is the first time, to our knowledge, that the iterative solution of the inverse elasticity problem has been implemented on experimentally acquired compression optical coherence elastography data.

We present a graphics processing unit (GPU)-accelerated optical coherence elastography (OCE) system capable of generating strain images (elastograms) of soft tissue at near video-rates. The system implements phase-sensitive compression OCE using a pipeline of GPU kernel functions to enable a highly parallel implementation of OCE processing using the OpenCL framework. Developed on a commercial-grade GPU and desktop computer, the system achieves a processing rate of 21 elastograms per second at an image size of 960 × 400 pixels, enabling high-rate visualization during acquisition. The system is demonstrated on both tissue-simulating phantoms and fresh ex vivo mouse muscle. To the best of our knowledge, this is the first implementation of near video-rate OCE and the fastest reported OCE processing rate, enabling, for the first time, a system capable of computing and displaying OCE elastograms interactively during acquisition. This advance provides new opportunities for medical imaging of soft tissue stiffness using optical methods.

In optical coherence elastography, images are formed by mapping a mechanical property of tissue. Such images, known as elastograms, are formed on the microscale, intermediate between that of cells and whole organs. Optical coherence elastography holds great promise for detecting and monitoring the altered mechanical properties that accompany many clinical conditions and pathologies, particularly in cancer, cardiovascular disease and eye disease. In this review, we first consider how the mechanical properties of tissue are linked with tissue function and pathology. We then describe currently prominent optical coherence elastography techniques, with emphasis on the methods of mechanical loading and displacement estimation. We highlight the sensitivity to microstrain deformations at tens of micrometer resolution. Throughout, optical coherence elastography is considered in the context of other elastography methods, mainly ultrasound elastography and magnetic resonance elastography. This context serves to highlight its advantages, early stage of development of applications, and strong prospects for future impact.

Four-wave mixing (FWM) between 2-ps pulses in a multiquantum-well semiconductor optical amplifier (SOA) is presented. The conjugate pulses are fully characterized using the frequency-resolved optical gating technique. The detuning between the pump and probe is varied, leading to a compression of the FWM signal from 3.71 to 2.77 ps as the detuning is increased from 5 to 25 nm. The output conjugate pulse is always broader than the injected probe signal due to gain saturation effects. A reshaping of the conjugate pulse is also measured. However, large nonlinearities are introduced to the frequency chirp across the pulse for large detunings which may degrade the performance of four-wave-mixing-based all-optical processing applications in SOAs.