Adaptive incremental method for strain estimation in phase-sensitive optical coherence elastography

High-resolution optical coherence tomography using gapped spectrum and real-valued iterative adaptive approach

Optical coherence tomography (OCT) is a powerful imaging technique that is capable of imaging cross-sectional structures with micrometer resolution. After combining with phase-sensitive detection, it can sense small changes in the physical quantities inside an object. In OCT, axial resolution is generally improved by expanding the bandwidth of the light source. However, when the bandwidth is expanded discontinuously, the wavelength gap induces abnormal sidelobes when estimating OCT signals in the depth domain. This problem can lead to poor axial resolution. Herein, we present a method based on a real-valued iterative adaptive approach (RIAA) to achieve a high axial resolution under a discontinuous bandwidth condition. The method uses a weighted matrix to suppress the abnormal sidelobes caused by the wavelength gap and, therefore, can realize high-resolution measurements. A single-reflector OCT spectrum was first measured for validation, and its amplitude in the depth domain was estimated using different methods. The results indicate that the RIAA had the best capability of suppressing abnormal sidelobes, thereby achieving a high axial resolution. In addition, cross-sectional images and phase-difference maps of three different samples were measured. A comparison of the results validated the practical value of this method.

Deep-learning-based approach for strain estimation in phase-sensitive optical coherence elastography

In this Letter, a deep-learning-based approach is proposed for estimating the strain field distributions in phase-sensitive optical coherence elastography. The method first uses the simulated wrapped phase maps and corresponding phase-gradient maps to train the strain estimation convolution neural network (CNN) and then employs the trained CNN to calculate the strain fields from measured phase-difference maps. Two specimens with different deformations, one with homogeneous and the other with heterogeneous, were measured for validation. The strain field distributions of the specimens estimated by different approaches were compared. The results indicate that the proposed deep-learning-based approach features much better performance than the popular vector method, enhancing the SNR of the strain results by 21.6 dB.

Temporal phase evolution OCT for measurement of tissue deformation in the human retina in-vivo

We demonstrate the use of temporal phase evolution (TPE-) OCT methods to evaluate retinal tissue deformation in-vivo over time periods of several seconds. A custom built spectral domain (SD)-OCT system with an integrated retinal tracker, ensuring stable imaging with sub-speckle precision, was used for imaging. TPE-OCT measures and images phase differences between an initial reference B-scan and each of the subsequent B-scans of the evaluated temporal sequence. In order to demonstrate the precision and repeatability of the measurements, retinal nerve fiber (RNF) tissue deformations induced by retinal vessels pulsating with the heartbeat were analyzed in several healthy subjects. We show TPE maps (M-scans of phase evolution as a function of position along B-scan trace vs. time) of wrapped phase data and corresponding deformation maps in selected regions of the RNF layer (RNFL) over the course of several cardiac cycles. A reproducible phase pattern is seen at each heartbeat cycle for all imaged volunteers. RNF tissue deformations near arteries and veins up to ∼ 1.6 µm were obtained with an average precision for a single pixel of about 30 nm. Differences of motion induced by arteries and veins are also investigated.