No abstract

Probing the mechanical properties of tissue on the microscale could aid in the identification of diseased tissues that are inadequately detected using palpation or current clinical imaging modalities, with potential to guide medical procedures such as the excision of breast tumours. Compression optical coherence elastography (OCE) maps tissue strain with microscale spatial resolution and can delineate microstructural features within breast tissues. However, without a measure of the locally applied stress, strain provides only a qualitative indication of mechanical properties. To overcome this limitation, we present quantitative micro-elastography, which combines compression OCE with a compliant stress sensor to image tissue elasticity. The sensor consists of a layer of translucent silicone with well-characterized stress-strain behaviour. The measured strain in the sensor is used to estimate the two-dimensional stress distribution applied to the sample surface. Elasticity is determined by dividing the stress by the strain in the sample. We show that quantification of elasticity can improve the ability of compression OCE to distinguish between tissues, thereby extending the potential for inter-sample comparison and longitudinal studies of tissue elasticity. We validate the technique using tissue-mimicking phantoms and demonstrate the ability to map elasticity of freshly excised malignant and benign human breast tissues.

We present a theoretical framework for strain estimation in optical coherence elastography (OCE), based on a statistical analysis of displacement measurements obtained from a mechanically loaded sample. We define strain sensitivity, signal-to-noise ratio and dynamic range, and derive estimates of strain using three methods: finite difference, ordinary least squares and weighted least squares, the latter implemented for the first time in OCE. We compare theoretical predictions with experimental results and demonstrate a ~12 dB improvement in strain sensitivity using weighted least squares compared to finite difference strain estimation and a ~4 dB improvement over ordinary least squares strain estimation. We present strain images (i.e., elastograms) of tissue-mimicking phantoms and excised porcine airway, demonstrating in each case clear contrast based on the sample’s elasticity.

We present optical coherence micro-elastography, an improved form of compression optical coherence elastography. We demonstrate the capacity of this technique to produce en face images, closely corresponding with histology, that reveal micro-scale mechanical contrast in human breast and lymph node tissues. We use phase-sensitive, three-dimensional optical coherence tomography (OCT) to probe the nanometer-to-micrometer-scale axial displacements in tissues induced by compressive loading. Optical coherence micro-elastography incorporates common-path interferometry, weighted averaging of the complex OCT signal and weighted least-squares regression. Using three-dimensional phase unwrapping, we have increased the maximum detectable strain eleven-fold over no unwrapping and the minimum detectable strain is 2.6 με. We demonstrate the potential of mechanical over optical contrast for visualizing micro-scale tissue structures in human breast cancer pathology and lymph node morphology.