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.

We review the development of phantoms for optical coherence tomography (OCT) designed to replicate the optical, mechanical and structural properties of a range of tissues. Such phantoms are a key requirement for the continued development of OCT techniques and applications. We focus on phantoms based on silicone, fibrin and poly(vinyl alcohol) cryogels (PVA-C), as we believe these materials hold the most promise for durable and accurate replication of tissue properties.

No abstract

An accurate intraoperative identification of malignant tissue is a challenge in the surgical management of breast cancer. Imaging techniques that help address this challenge could contribute to more complete and accurate tumor excision, and thereby help reduce the current high reexcision rates without resorting to the removal of excess healthy tissue. Optical coherence microelastography (OCME) is a three-dimensional, high-resolution imaging technique that is sensitive to microscale variations of the mechanical properties of tissue. As the tumor modifies the mechanical properties of breast tissue, OCME has the potential to identify, on the microscale, involved regions of fresh, unstained tissue. OCME is based on the use of optical coherence tomography (OCT) to measure tissue deformation in response to applied mechanical compression. In this feasibility study on 58 ex vivo samples from patients undergoing mastectomy or wide local excision, we demonstrate the performance of OCME as a means to visualize tissue microarchitecture in benign and malignant human breast tissues. Through a comparison with corresponding histology and OCT images, OCME is shown to enable ready visualization of features such as ducts, lobules, microcysts, blood vessels, and arterioles and to identify invasive tumor through distinctive patterns in OCME images, often with enhanced contrast compared with OCT. These results lay the foundation for future intraoperative studies. Cancer Res; 75(16); 3236–45. ©2015 AACR.

We present optical palpation, a tactile imaging technique for mapping micrometer- to millimeter-scale mechanical variations in soft tissue. In optical palpation, a stress sensor consisting of translucent, compliant silicone with known stress-strain behavior is placed on the tissue surface and a compressive load is applied. Optical coherence tomography (OCT) is used to measure the local strain in the sensor, from which the local stress at the sample surface is calculated and mapped onto an image. We present results in tissue-mimicking phantoms, demonstrating the detection of a feature embedded 4.7 mm below the sample surface, well beyond the depth range of OCT. We demonstrate the use of optical palpation to delineate the boundary of a region of tumor in freshly excised human breast tissue, validated against histopathology.