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Saturation artifacts in optical coherence tomography (OCT) occur when received signal exceeds the dynamic range of spectrometer. Saturation artifact shows a streaking pattern and could impact the quality of OCT images, leading to inaccurate medical diagnosis. In this paper, we automatically localize saturation artifacts and propose an artifact correction method via inpainting. We adopt a dictionary-based sparse representation scheme for inpainting. Experimental results demonstrate that, in both case of synthetic artifacts and real artifacts, our method outperforms interpolation method and Euler’s elastica method in both qualitative and quantitative results. The generic dictionary offers similar image quality when applied to tissue samples which are excluded from dictionary training. This method may have the potential to be widely used in a variety of OCT images for the localization and inpainting of the saturation artifacts., Graphical Abstract

An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma. However, due to the presence of the optic disc, the anatomical structure of the peripapillary region of the retina is complicated and is challenging for segmentation. To address this issue, we develop a novel graph convolutional network (GCN)-assisted two-stage framework to simultaneously label the nine retinal layers and the optic disc. Specifically, a multi-scale global reasoning module is inserted between the encoder and decoder of a U-shape neural network to exploit anatomical prior knowledge and perform spatial reasoning. We conduct experiments on human peripapillary retinal OCT images. We also provide public access to the collected dataset, which might contribute to the research in the field of biomedical image processing. The Dice score of the proposed segmentation network is 0.820 ± 0.001 and the pixel accuracy is 0.830 ± 0.002, both of which outperform those from other state-of-the-art techniques.

We report on the investigation of spectral leakage’s impact on the reconstruction of Fourier-domain optical coherence tomography (FD-OCT). We discuss the shift-variant nature introduced by the spectral leakage and develop a novel spatial-domain FD-OCT image formation model. A proof-of-concept phantom experiment is conducted to validate our model. Compared with previous models, the proposed framework could better describe the image formation process, especially when the fineness of the axial structure approaches the theoretical resolution limit.

Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment. In cardiac imaging, OCT has been used in assessing plaques before and after stenting. While needed in many scenarios, high resolution comes at the costs of demanding optical design and data storage/transmission. In OCT, there are two types of resolutions to characterize image quality: optical and digital resolutions. Although multiple existing works have heavily emphasized on improving the digital resolution, the studies on improving optical resolution or both resolutions remain scarce. In this paper, we focus on improving both resolutions. In particular, we investigate a deep learning method to address the problem of generating a high-resolution (HR) OCT image from a low optical and low digital resolution (L2R) image. To this end, we have modified the existing super-resolution generative adversarial network (SR-GAN) for OCT image reconstruction. Experimental results from the human coronary OCT images have demonstrated that the reconstructed images from highly compressed data could achieve high structural similarity and accuracy in comparison with the HR images. Besides, our method has obtained better denoising performance than the block-matching and 3D filtering (BM3D) and Denoising Convolutional Neural Networks (DnCNN) denoising method.

We investigate the signal digitization procedure in FD-OCT and propose a novel mixed-signal framework to model its image formation. It turns out FD-OCT is a shift-variant system, if conventional IDFT is used to reconstruct the images.

We propose and experimentally demonstrate a multiple input multiple output - artificial neural network (MIMO-ANN) nonlinear equalizer (NLE) to process the complex quadrature amplitude modulation (QAM) signal in a single-sideband (SSB) self-coherent detection (SCD) system. In the proposed scheme, a 2-by-2 MIMO structure with two ANNs is employed to effectively mitigate the signal distortions induced by in-phase and quadrature (IQ) imbalance and fiber nonlinear effects. By using the proposed MIMO-ANN NLE, we successfully transmit a 112-Gb/s SSB 16-QAM signal over a single-span 120-km single mode fiber (SMF) in a direct detection (DD) system with a bit error rate (BER) lower than 3.8 × 10−3. We also conduct a comparative study between the proposed MIMO-ANN NLE, a feedforward equalizer (FFE), a NLE consisting of two independent real-valued Volterra filters, and a MIMO-Volterra filter. The proposed MIMO-ANN NLE outperforms other equalizers with the longer fiber length and thus stronger nonlinearities, since it can easily approximate a complicated nonlinear function. To the best of our knowledge, this is the first experimental demonstration of an ANN-based equalizer in an SSB SCD system.

Background and objective Cilia-driven mucociliary clearance is an important self-defense mechanism of great clinical importance in pulmonary research. Conventional light microscopy possesses the capability to visualize individual cilia and its beating pattern but lacks the throughput to assess the global ciliary activities and flow dynamics. Optical coherence tomography (OCT), which provides depth-resolved cross-sectional images, was recently introduced to this area. Materials and methods Fourteen de-identified human tracheobronchial tissues are directly imaged by two OCT systems: one system centered at 1,300 nm with 6.5 μm axial resolution and 15 μm lateral resolution, and the other centered at 800 nm with 2.72 μm axial resolution and 5.52 μm lateral resolution. Speckle variance images are obtained in both cross-sectional and volumetric modes. After imaging, sample blocks are sliced along the registered OCT imaging plane and processed with hematoxylin and eosin (H&E) stain for comparison. Quantitative flow analysis is performed by tracking the path-lines of microspheres in a fixed cross-section. Both the flow rate and flow direction are characterized. Results The speckle variance images successfully segment the ciliated epithelial tissue from its cilia-denuded counterpart, and the results are validated by corresponding H&E stained sections. A further temporal frequency analysis is performed to extract the ciliary beat frequency (CBF) at cilia cites. By adding polyester microspheres as contrast agents, we demonstrate ex vivo imaging of the flow induced by cilia activities of human tracheobronchial samples. Conclusion This manuscript presents an ex vivo study on human tracheobronchial ciliated epithelium and its induced mucous flow by using OCT. Within OCT images, intact ciliated epithelium is effectively distinguished from cilia-denuded counterpart, which serves as a negative control, by examining the speckle variance images. The cilia beat frequency is extracted by temporal frequency analysis. The flow rate, flow direction, and particle throughput are obtained through particle tracking. The availability of these quantitative parameters provides us with a powerful tool that will be useful for studying the physiology, pathophysiology and the effectiveness of therapies on epithelial cilia function, as well as serve as a diagnostic tool for diseases associated with ciliary dysmotility. Lasers Surg. Med. 49:270-279, 2017. © 2017 Wiley Periodicals, Inc.

We propose and experimentally demonstrate a neural-network-based wavelength locking algorithm for a 1 × 2 silicon microring switch. The wavelength locking is performed at a 20-nm/ms locking speed over a full free spectral range.

Here we present a novel phase-sensitive swept-source optical coherence tomography (PhS-SS-OCT) system. The simultaneously recorded calibration signal, which is commonly used in SS-OCT to stabilize the phase, is randomly sub-sampled during the acquisition, and it is later reconstructed based on the Compressed Sensing (CS) theory. We first mathematically investigated the method, and verified it through computer simulations. We then conducted a vibrational frequency test and a flow velocity measurement in phantoms to demonstrate the system's capability of handling phase-sensitive tasks. The proposed scheme shows excellent phase stability with greatly discounted data bandwidth compared with conventional procedures. We further showcased the usefulness of the system in biological samples by detecting the blood flow in ex vivo swine left marginal artery. The proposed system is compatible with most of the existing SS-OCT systems and could be a preferred solution for future high-speed phase-sensitive applications.

A single-channel high-resolution cross-polarization (CP) optical coherence tomography (OCT) system is presented for multicontrast imaging of human myocardium in one-shot measurement. The intensity and functional contrasts, including the ratio between the cross- and co-polarization channels as well as the cumulative retardation, are reconstructed from the CP-OCT readout. By comparing the CP-OCT results with histological analysis, it is shown that the system can successfully delineate microstructures in the myocardium and differentiate the fibrotic myocardium from normal or ablated myocardium based on the functional contrasts provided by the CP-OCT system. The feasibility of using A-line profiles from the 2 orthogonal polarization channels to identify fibrotic myocardium, normal myocardium and ablated lesion is also discussed.

Sparse representation theory is an exciting area of research with recent applications in medical imaging and detection, segmentation, and quantitative analysis of biological processes. We present a variant on the robust-principal component analysis (RPCA) algorithm, called frequency constrained RPCA (FC-RPCA), for selectively segmenting dynamic phenomena that exhibit spectra within a user-defined range of frequencies. The algorithm lacks subjective parameter tuning and demonstrates robust segmentation in datasets containing multiple motion sources and high amplitude noise. When tested on 17 ex-vivo, time lapse optical coherence tomography (OCT) B-scans of human ciliated epithelium, segmentation accuracies ranged between 91-99% and consistently out-performed traditional RPCA.

We propose a new model to characterize the phase noise in swept-source optical coherence tomography (SS-OCT). The new model explicitly incorporates scanning variability, timing jitter, and sample location in addition to intensity noise (shot noise). The model was analyzed and validated by using both Monte Carlo methods and experiments. We suggest that the proposed model can be used as a guideline for future SS-OCT experimental designs.