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With the rapid advances of light source technology, the A-line imaging rate of swept-source optical coherence tomography (SS-OCT) has experienced a great increase in the past three decades. The bandwidths of data acquisition, data transfer, and data storage, which can easily reach several hundred megabytes per second, have now been considered major bottlenecks for modern SS-OCT system design. To address these issues, various compression schemes have been previously proposed. However, most of the current methods focus on enhancing the capability of the reconstruction algorithm and can only provide a data compression ratio (DCR) up to 4 without impairing the image quality. In this Letter, we proposed a novel design paradigm, in which the sub-sampling pattern for interferogram acquisition is jointly optimized with the reconstruction algorithm in an end-to-end manner. To validate the idea, we retrospectively apply the proposed method on an ex vivo human coronary optical coherence tomography (OCT) dataset. The proposed method could reach a maximum DCR of ∼62.5 with peak signal-to-noise ratio (PSNR) of 24.2 dB, while a DCR of ∼27.78 could yield a visually pleasant image with a PSNR of ∼24.6 dB. We believe the proposed system could be a viable remedy for the ever-growing data issue in SS-OCT.

Learning-based computer-generated holography (CGH) algorithms appear as novel alternatives to generate phase-only holograms. However, most existing learning-based approaches underperform their iterative peers regarding display quality. Here, we recognize that current convolutional neural networks have difficulty learning cross-domain tasks due to the limited receptive field. In order to overcome this limitation, we propose a Fourier-inspired neural module, which can be easily integrated into various CGH frameworks and significantly enhance the quality of reconstructed images. By explicitly leveraging Fourier transforms within the neural network architecture, the mesoscopic information within the phase-only hologram can be more handily extracted. Both simulation and experiment were performed to showcase its capability. By incorporating it into U-Net and HoloNet, the peak signal-to-noise ratio of reconstructed images is measured at 29.16 dB and 33.50 dB during the simulation, which is 4.97 dB and 1.52 dB higher than those by the baseline U-Net and HoloNet, respectively. Similar trends are observed in the experimental results. We also experimentally demonstrated that U-Net and HoloNet with the proposed module can generate a monochromatic 1080p hologram in 0.015 s and 0.020 s, respectively.

The image reconstruction for Fourier-domain optical coherence tomography (FD-OCT) could be achieved by iterative methods, which offer a more accurate estimation than the traditional inverse discrete Fourier transform (IDFT) reconstruction. However, the existing iterative methods are mostly A-line-based and are developed on CPU, which causes slow reconstruction. Besides, A-line-based reconstruction makes the iterative methods incompatible with most existing image-level image processing techniques. In this paper, we proposed an iterative method that enables B-scan-based OCT image reconstruction, which has three major advantages: (1) Large-scale parallelism of the OCT dataset is achieved by using GPU acceleration. (2) A novel image-level cross-domain regularizer was developed, such that the image processing could be performed simultaneously during the image reconstruction; an enhanced image could be directly generated from the OCT interferogram. (3) The scalability of the proposed method was demonstrated for 3D OCT image reconstruction. Compared with the state-of-the-art (SOTA) iterative approaches, the proposed method achieves higher image quality with reduced computational time by orders of magnitude. To further show the image enhancement ability, a comparison was conducted between the proposed method and the conventional workflow, in which an IDFT reconstructed OCT image is later processed by a total variation-regularized denoising algorithm. The proposed method can achieve a better performance evaluated by metrics such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), while the speed is improved by more than 30 times. Real-time image reconstruction at more than 20 B-scans per second was realized with a frame size of 4096 (axial) × 1000 (lateral), which showcases the great potential of the proposed method in real-world applications.

Monte Carlo (MC) simulation has been widely used to study imaging procedures, including Fourier-domain optical coherence tomography (FD-OCT). Despite the broadband nature of FD-OCT, the results obtained at a single wavelength are often used in previous studies. Some wavelength-relied imaging applications, such as spectroscopic OCT (S-OCT), are unlikely to be simulated in this way due to the lack of information from the entire spectrum. Here, we propose a novel simulator for full-wavelength MC simulation of FD-OCT. All wavelengths within the emission spectrum of the light source will be simulated, and the optical properties derived from Mie theory will be applied. We further combine the inverse discrete Fourier transform (IDFT) with a probability distribution-based signal pre-processing to combat the excessive noises in the OCT signal reconstruction, which is caused by the non-uniform distribution of the scattering events at different wavelengths. Proof-of-concept simulations are conducted to show the excellent performance of the proposed simulator on signal reconstruction and optical properties extraction. Compared with the conventional method, the proposed simulator is more accurate and could better preserve the wavelength-dependent features. For example, the mean square error (MSE) computed between the backscattering coefficient extracted by the proposed simulator and the ground truth is 0.11, which is far less than the value (7.67) of the conventional method. We believe this simulator could be an effective tool to study the wavelength dependency in FD-OCT imaging as well as a preferred solution for simulating spectroscopic OCT.

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

Accurate retinal layer segmentation, especially the peripapillary retinal nerve fiber layer (RNFL) is critical for the diagnosis of ophthalmic diseases. However, due to the complex morphologies of the peripapillary region, most of the existing methods focus on segmenting the macular region and could not be directly applied to the peripapillary retinal optical coherence tomography (OCT) images. In this paper, we propose a novel graph convolutional network (GCN)-assisted segmentation framework based on a U-shape neural network for peripapillary retinal layer segmentation in OCT images. We argue that the strictly stratified structure of retina layers in addition to the centered optic disc is an ideal objective for GCN. Specifically, a graph reasoning block is inserted between the encoder and decoder of the U-shape neural network to conduct spatial reasoning. In this way, the peripapillary retina in OCT images is segmented into nine layers including RNFL. The proposed method was trained and tested on our collected dataset of peripapillary retinal OCT images. Experimental results showed that our segmentation method outperformed other state-of-the-art methods. In particular, compared with ReLayNet, the average and RNFL Dice coefficients are improved by 1.2% and 2.6%, respectively.

FD-OCT is a widely used technology which could provide high-resolution 3D reconstruction images. Conventional OCT uses IDFT reconstruction method, which could obtain an FFT-limited axial resolution. Recently, several optimization-based methods have reportedly achieved a resolution improvement, while undesired noise artifacts might appear in their result, which could degrade the image quality. In this work, we proposed an iterative error reduction method in order to remove the artifacts as well as improve the resolution. A numerical simulation is designed to validate our algorithm. Two reconstruction methods including IDFT reconstruction, and l1 minimization are selected for a comparative study. Specifically, we conduct the simulation at four different noise levels. The result shows that our proposed method could greatly suppress the artifact and obtain a great reconstruction even when the SNR is reduced.

Here, we analytically study the signal digitization procedure in FD-OCT and propose a novel mixed-signal framework to model its time-domain image formation. It turns out that FD-OCT is a shift-variant system, if the conventional IDFT-based technique is used to reconstruct the A-lines. Specifically, both amplitude and phase responses of the system are dependent on the axial location of the input sample. We believe this finding could provide us with new insights towards the image reconstruction of FD-OCT and guide researchers to develop better reconstruction algorithms in the future.

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.

Subcellular resolution is required for OCT to portray the microstructural information of myocardium issue that is comparable to histology. Compare with its intrinsic intensity contrast, functional OCT system may provide contrast related to the tissue composition. We present a high-resolution (HR) cross-polarization OCT system that can provide functional contrast of human myocardium tissue in one-shot measurement. The system is implemented based on our previously reported high-resolution long imaging range OCT system with minimal modification. It features a broadband supercontinuum source, single-channel and one-shot detection, with moderate signal processing. The system has an axial resolution of 3.07 μm, and it is capable to produce accurate polarization information by calibrating the reconstruction performance with a quarter wave plate. The orthogonal polarization channels are multiplexed to fit within one imaging range. Following CP-OCT detection, the retardation can be reconstructed based on the complex signals, and the depolarization effect can be depicted by the channel intensity ratio. Tissue specimens from ten fresh human hearts are used to demonstrate the capability of CP-OCT contrasts. By analyzing the intrinsic and functional OCT contrasts of fresh human myocardium tissues against histology slides, we show that various tissue structures and tissue types of the myocardium, such as fibrosis and ablated lesions, can be better depicted by the function contrasts. We also suggest the possibility of using A-line features from the two orthogonal polarization channels to distinguish normal myocardium, fibrotic myocardium, and ablated lesions. This may serve as a rapid and cost-efficient solution for assessment of myocardium and further facilitate automatic tissue classification.

Optical Coherence Tomography (OCT) has established itself as an important tool for studying the role of cilia in Mucociliary Clearance (MCC) due to its ability to observe the cilia’s temporal characteristics over a large field of view. To obtain useful, quantitative measures of this dynamic morphology, the ciliated layer of tissue needs to be segmented from other static components. This is currently accomplished using Speckle Variance processing, a technique whose success relies on subjective thresholding and lacks sensitivity to other sources of speckle noise. We present a modified, frequency constrained, version of Robust Principle Component Analysis (RPCA) which we call Frequency Constrained RPCA (FC-RPCA) as an alternative method for dynamic segmentation of cilia from time-varying OCT B-scans. Based in Sparse Representation theory, FC-RPCA decomposes stacks of images in time into low-rank (static) and a sparse (dynamic) matrices. The sparse matrix represents the segmented cilia layer because of the sparse frequency spectrum exhibited by their characteristic beating pattern. This novel algorithm introduces an additional feature, a user defined frequency constraint on the sparse component, which prevents other sources of speckle noise, like slow moving mucus clouds at the tissues surface, from being segmented with the cilia. The algorithm was used to segment motile cilia in 17 datasets of ex-vivo human ciliated epithelium with high accuracy. Furthermore, FC-RPCA requires no parameter tuning across datasets, demonstrating its capability as a robust tool for processing large volumes of data. When compared with the standard Speckle Variance method, FC-RPCA performed with improved accuracy and selectivity.

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.

Optical coherence tomography (OCT) is a promising candidate for observing dynamic processes via time-lapse imaging. We suggested that a faithful recovery of the dynamic signals could be achieved based on a low-rank plus sparse (L+S) reconstruction of under-sampled data.

Phase-resolved optical coherence tomography (OCT), a functional extension of OCT, provides depth-resolved phase information with extra contrast. In cardiology, changes in the mechanical properties have been associated with tissue remodeling and disease progression. Here we present the capability of profiling structural deformation of the sample in vivo by using a highly stable swept source OCT system The system, operating at 1300 nm, has an A-line acquisition rate of 200 kHz. We measured the phase noise floor to be 6.5 pm±3.2 pm by placing a cover slip in the sample arm, while blocking the reference arm. We then conducted a vibrational frequency test by measuring the phase response from a polymer membrane stimulated by a pure tone acoustic wave from 10 kHz to 80 kHz. The measured frequency response agreed with the known stimulation frequency with an error < 0.005%. We further measured the phase response of 7 fresh swine hearts obtained from Green Village Packing Company through a mechanical stretching test, within 24 hours of sacrifice. The heart tissue was cut into a 1 mm slices and fixed on two motorized stages. We acquired 100,000 consecutive M-scans, while the sample is stretched at a constant velocity of 10 um/s. The depth-resolved phase image presents linear phase response over time at each depth, but the slope varies among tissue types. Our future work includes refining our experiment protocol to quantitatively measured the elastic modulus of the tissue in vivo and building a tissue classifier based on depth-resolved phase information.

Functional extensions to optical coherence tomography (OCT) provide useful imaging contrasts that are complementary to conventional OCT. Our goal is to characterize tissue types within the myocardial due to remodeling and therapy. High-speed imaging is necessary to extract mechanical properties and dynamics of fiber orientation changes in a beating heart. Functional extensions of OCT such as polarization sensitive and optical coherence elastography (OCE) require high phase stability of the system, which is a drawback of current mechanically tuned swept source OCT systems. Here we present a high-speed functional imaging platform, which includes an ultrahigh-phase-stable swept source equipped with KTN deflector from NTT-AT. The swept source does not require mechanical movements during the wavelength sweeping; it is electrically tuned. The inter-sweep phase variance of the system was measured to be less than 300 ps at a path length difference of ~2 mm. The axial resolution of the system is 20 µm and the -10 dB fall-off depth is about 3.2 mm. The sample arm has an 8 mmx8 mm field of view with a lateral resolution of approximately 18 µm. The sample arm uses a two-axis MEMS mirror, which is programmable and capable of scanning arbitrary patterns at a sampling rate of 50 kHz. Preliminary imaging results showed differences in polarization properties and image penetration in ablated and normal myocardium. In the future, we will conduct dynamic stretching experiments with strips of human myocardial tissue to characterize mechanical properties using OCE. With high speed imaging of 200 kHz and an all-fiber design, we will work towards catheter-based functional imaging.

We study one-dimensional photonic crystals made of cholesteric liquid crystals with sandwiched isotropic defect layers. Based on the Berreman Fast 4 × 4 matrix method, the dispersion relation of one-dimensional photonic crystals is calculated and the corresponding reflection chromaticity is obtained. It is found that the color shift could be controlled by adjusting the thickness and refractive index of the isotropic defect layers. Compared with conventional structures, the reflection chromaticity of this structure is insensitive to the incident angle, if the thickness ratio of the cholesteric liquid crystals to that of the isotropic defect layers and the refractive index of periodical isotropic defect layers are properly set. Furthermore, the common forbidden bands for both left and right circular polarizations can be obtained, and we also take the wavelength-dependent refractive indices into consideration and obtain the reflected light chromaticity with the incident angle increasing. The proposed device can be used as a reflective color filter in the display industry.

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.

A novel flat-pane-display based on interference modulation for both intensity and color is presented. The system has the potential to sextuple the optical efficiency of the current TFT-LCD technology, and an improved configuration can achieve comparable display quality as that of TFT-LCD, enabling it as a promising display solution to handheld devices.

We introduce a fluorescent imaging method that is capable of detecting fluorescent micro-particles over an ultra-wide field of view of 19 cm × 28 cm using a modified flatbed scanner. We added a custom-designed absorbing emission filter, a computer controlled two dimensional LED array, and modified the driver of the scanner to maximize the sensitivity, exposure time, and gain for fluorescent detection of micro-objects. This high-throughput fluorescent imaging device used in conjunction with a microfluidic sample holder enables rapid screening of fluorescent micro-objects inside more than 2.2mL of optically dense media (i.e., whole blood) within 5 minutes. The device is sensitive enough to detect fluorescently labeled cells, and generates images that have an effective pixel count of 2.2 Giga-pixels.

We demonstrate a new fluorescent imaging technique that can screen for fluorescent micro-objects over an ultra-wide field-of-view (FOV) of ~532 cm(2), i.e., 19 cm × 28 cm, reaching a space-bandwidth product of more than 2 billion. For achieving such a large FOV, we modified the hardware and software of a commercially available flatbed scanner, and added a custom-designed absorbing fluorescent filter, a two-dimensional array of external light sources for computer-controlled and high-angle fluorescent excitation. We also re-programmed the driver of the scanner to take full control of the scanner hardware and achieve the highest possible exposure time, gain and sensitivity for detection of fluorescent micro-objects through the gradient index self-focusing lens array that is positioned in front of the scanner sensor chip. For example, this large FOV of our imaging platform allows us to screen more than 2.2 mL of undiluted whole blood for detection of fluorescent micro-objects within

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.