Quantitative Mapping of Strains and Young Modulus Based on Phase-Sensitive OCT

In this chapter we consider mapping of local strains and tissue elasticity in optical coherence tomography (OCT) based on analysis of phase-sensitive OCT scans. Conventional structural OCT scans correspond to spatially resolved mapping of the backscattering intensity of the probing optical beam. Deeper analysis of such sequentially acquired multiple OCT scans can be used to extract additional information about motion of scatterers in the examined region. Such detailed analysis of OCT scans has already resulted in creation of OCT-based visualization of blood microcirculation, which has been implemented in several commercially available devices, especially for ophthalmic applications. Another functional extension of OCT emerging in recent years is the OCT-based elastography, i.e., mapping of local strains and elastic properties in the imaged region. Here, we describe the main principles of local strain mapping in phase-sensitive OCT with a special focus on the recently proposed efficient vector method of estimation of interframe phase-variation gradients. The initially performed mapping of local strains is then used for realization of quantitative compressional elastography, i.e., mapping of the Young modulus and obtaining stress-strain dependences for the studied samples. The discussed principles are illustrated by simulated and experimental examples of elastographic OCT-based visualization. The presented elastographic principles are rather general and can be used in a wide area of biomedical and technical applications

Real-Time Strain and Elasticity Imaging in Phase-Sensitive Optical Coherence Elastography Using a Computationally Efficient Realization of the Vector Method

We present a real-time realization of OCT-based elastographic mapping local strains and distribution of the Young’s modulus in biological tissues, which is in high demand for biomedical usage. The described variant exploits the principle of Compression Optical Coherence Elastography (C-OCE) and uses processing of phase-sensitive OCT signals. The strain is estimated by finding local axial gradients of interframe phase variations. Instead of the popular least-squares method for finding these gradients, we use the vector approach, one of its advantages being increased computational efficiency. Here, we present a modified, especially fast variant of this approach. In contrast to conventional correlation-based methods and previously used phase-resolved methods, the described method does not use any search operations or local calculations over a sliding window. Rather, it obtains local strain maps (and then elasticity maps) using several transformations represented as matrix operations applied to entire complex-valued OCT scans. We first elucidate the difference of the proposed method from the previously used correlational and phase-resolved methods and then describe the proposed method realization in a medical OCT device, in which for real-time processing, a “typical” central processor (e.g., Intel Core i7-8850H) is sufficient. Representative examples of on-flight obtained elastographic images are given. These results open prospects for broad use of affordable OCT devices for high-resolution elastographic vitalization in numerous biomedical applications, including the use in clinic

Histological validation of in vivo assessment of cancer tissue inhomogeneity and automated morphological segmentation enabled by Optical Coherence Elastography

Abstract We present a non-invasive (albeit contact) method based on Optical Coherence Elastography (OCE) enabling the in vivo segmentation of morphological tissue constituents, in particular, monitoring of morphological alterations during both tumor development and its response to therapies. The method uses compressional OCE to reconstruct tissue stiffness map as the first step. Then the OCE-image is divided into regions, for which the Young’s modulus (stiffness) falls in specific ranges corresponding to the morphological constituents to be discriminated. These stiffness ranges (characteristic "stiffness spectra") are initially determined by careful comparison of the "gold-standard" histological data and the OCE-based stiffness map for the corresponding tissue regions. After such pre-calibration, the results of morphological segmentation of OCE-images demonstrate a striking similarity with the histological results in terms of percentage of the segmented zones. To validate the sensitivity of the OCE-method and demonstrate its high correlation with conventional histological segmentation we present results obtained in vivo on a murine model of breast cancer in comparative experimental study of the efficacy of two antitumor chemotherapeutic drugs with different mechanisms of action. The new technique allowed in vivo monitoring and quantitative segmentation of (1) viable, (2) dystrophic, (3) necrotic tumor cells and (4) edema zones very similar to morphological segmentation of histological images. Numerous applications in other experimental/clinical areas requiring rapid, nearly real-time, quantitative assessment of tissue structure can be foreseen