28 MHz swept source at 1.0 μm for ultrafast quantitative phase imaging

Studying the efficacy of antiplatelet drugs on atherosclerosis by optofluidic imaging on a chip

Vascular stenosis caused by atherosclerosis instigates activation and aggregation of platelets, eventually resulting in thrombus formation. Although antiplatelet drugs are commonly used to inhibit platelet activation and aggregation, they unfortunately cannot prevent recurrent thrombotic events in patients with atherosclerosis. This is partially due to the limited understanding of the efficacy of antiplatelet drugs in the complex hemodynamic environment of vascular stenosis. Conventional methods for evaluating the efficacy of antiplatelet drugs under stenosis either fail to simulate the hemodynamic environment of vascular stenosis characterized by high shear stress and recirculatory flow or lack spatial resolution in their analytical techniques to statistically identify and characterize platelet aggregates. Here we propose and experimentally demonstrate a method comprising an in vitro 3D stenosis microfluidic chip and an optical time-stretch quantitative phase imaging system for studying the efficacy of antiplatelet drugs under stenosis. Our method simulates the atherogenic flow environment of vascular stenosis while enabling high-resolution and statistical analysis of platelet aggregates. Using our method, we distinguished the efficacy of three antiplatelet drugs, acetylsalicylic acid (ASA), cangrelor, and eptifibatide, for inhibiting platelet aggregation induced by stenosis. Specifically, ASA failed to inhibit stenosis-induced platelet aggregation, while eptifibatide and cangrelor showed high and moderate efficacy, respectively. Furthermore, we demonstrated that the drugs tested also differed in their efficacy for inhibiting platelet aggregation synergistically induced by stenosis and agonists (e.g., adenosine diphosphate, and collagen). Taken together, our method is an effective tool for investigating the efficacy of antiplatelet drugs under vascular stenosis, which could assist the development of optimal pharmacologic strategies for patients with atherosclerosis.

Highly coherent, flat, and broadband time-stretched swept source based on extra-cavity spectral shaping assisted by a booster semiconductor optical amplifier

We demonstrate a flat broadband time-stretched swept source based on extra-cavity spectral shaping. By adjusting the polarization-dependent gain profile and driving current of the booster optical amplifier (BOA), extra-cavity spectral shaping is optimized to generate output with a 1-dB bandwidth of ∼100 nm, 3-dB bandwidth of ∼140 nm and output power of ∼21.4 mW. The short-term and long-term stabilities are characterized. The average cross correlation of 183,485 round trips is 0.9997 with a standard deviation of 2×10^-5, indicating high single-shot spectral similarity and high coherence. The noise floor of relative spectral energy jitter is -141.7 dB/Hz, indicating a high short-term spectral energy stability. The proposed highly stable flat broadband time-stretched swept source is applied to an optical coherence tomography (OCT) system. The axial resolution is 10.8 µm. The proposed swept source can serve as excellent light sources in ultra-fast coherent detection systems for high precision sensing and imaging.

Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data

Optical coherence tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical setup and can be easily integrated with existing swept-source or spectral-domain OCT systems to reduce the amount of raw spectral data to be acquired. To show the efficacy of this framework, we trained and blindly tested a deep neural network using mouse embryo samples imaged by an SS-OCT system. Using 2-fold undersampled spectral data (i.e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in 0.59 ms using multiple graphics-processing units (GPUs), removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i.e., 1280 spectral points per A-line). We also successfully demonstrate that this framework can be further extended to process 3× undersampled spectral data per A-line, with some performance degradation in the reconstructed image quality compared to 2× spectral undersampling. Furthermore, an A-line-optimized undersampling method is presented by jointly optimizing the spectral sampling locations and the corresponding image reconstruction network, which improved the overall imaging performance using less spectral data points per A-line compared to 2× or 3× spectral undersampling results. This deep learning-enabled image reconstruction approach can be broadly used in various forms of spectral-domain OCT systems, helping to increase their imaging speed without sacrificing image resolution and signal-to-noise ratio.

1.1-µm Band Extended Wide-Bandwidth Wavelength-Swept Laser Based on Polygonal Scanning Wavelength Filter

We demonstrated a 1.1-µm band extended wideband wavelength-swept laser (WSL) that combined two semiconductor optical amplifiers (SOAs) based on a polygonal scanning wavelength filter. The center wavelengths of the two SOAs were 1020 nm and 1140 nm, respectively. Two SOAs were connected in parallel in the form of a Mach-Zehnder interferometer. At a scanning speed of 1.8 kHz, the 10-dB bandwidth of the spectral output and the average power were approximately 228 nm and 16.88 mW, respectively. Owing to the nonlinear effect of the SOA, a decrease was observed in the bandwidth according to the scanning speed. Moreover, the intensity of the WSL decreased because the oscillation time was smaller than the buildup time. In addition, a cholesteric liquid crystal (CLC) cell was fabricated as an application of WSL, and the dynamic change of the first-order reflection of the CLC cell in the 1-µm band was observed using the WSL. The pitch jumps of the reflection band occurred according to the electric field applied to the CLC cell, and instantaneous changes were observed.

Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry

Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology, instruments based on the photonic time stretch have established record real-time measurement throughput in spectroscopy, optical coherence tomography, and imaging flow cytometry. These extreme-throughput instruments generate approximately 1 Tbit/s of continuous measurement data and have led to the discovery of rare phenomena in nonlinear and complex systems as well as new types of biomedical instruments. Owing to the abundance of data they generate, time-stretch instruments are a natural fit to deep learning classification. Previously we had shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time-stretch microscopy, image processing and feature extraction, followed by deep learning for finding cancer cells in the blood. Such a technology holds promise for early detection of primary cancer or metastasis. Here we describe a new deep learning pipeline, which entirely avoids the slow and computationally costly signal processing and feature extraction steps by a convolutional neural network that directly operates on the measured signals. The improvement in computational efficiency enables low-latency inference and makes this pipeline suitable for cell sorting via deep learning. Our neural network takes less than a few milliseconds to classify the cells, fast enough to provide a decision to a cell sorter for real-time separation of individual target cells. We demonstrate the applicability of our new method in the classification of OT-II white blood cells and SW-480 epithelial cancer cells with more than 95% accuracy in a label-free fashion.

Compressed sensing-enabled phase-sensitive swept-source optical coherence tomography

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.

Single-shot photonic time-stretch digitizer using a dissipative soliton-based passively mode-locked fiber laser

We demonstrate a single-shot photonic time-stretch digitizer using a dissipative soliton-based passively mode-locked fiber laser. The theoretical analysis and simulation results indicate that the dissipative soliton-based optical source with a flat spectrum relieves the envelope-induced signal distortion, and its high energy spectral density helps to improve the signal-to-noise ratio, both of which are favorable for simplifying the optical front-end architecture of a photonic time-stretch digitizer. By employing a homemade dissipative soliton-based passively mode-locked erbium-doped fiber laser in a single-shot photonic time-stretch digitizer, an effective number of bits of 4.11 bits under an effective sampling rate of 100 GS/s is experimentally obtained without optical amplification in the link and pulse envelope removing process.

High-speed wavelength-swept source at 2.0 μm and its application in imaging through a scattering medium

We report a high-speed wavelength-swept source operating at 2.0 μm through advanced time-stretch technology. It sweeps over 30 nm at a speed of 3.3×10⁹  nm/s and a repetition rate of ∼19  MHz. To the best of our knowledge, this is the first time that a megahertz-stable swept source has been implemented at such a long wavelength. A wide bandwidth is enabled by a simple mode-locked fiber laser that covers a wavelength range of ∼60  nm. The all-optical wavelength sweeping is realized by a chirped fiber Bragg grating (CFBG), which shows a superior temporal stability and power efficiency, compared with commonly used dispersive fibers, particularly in the 2.0 μm wavelength window. To showcase its specialties, here we employ it to perform high-speed spectrally-encoded microscopy (i.e., time-stretch imaging) through a scattering medium at a line-scan rate of up to ∼19  MHz. Better image quality is achieved, compared with a conventional imaging window at 1.0 μm. It is believed that the potential applications of this new high-speed swept source will benefit the transient diagnosis that requires deep penetration through a scattering medium.

All-passive pixel super-resolution of time-stretch imaging

Based on image encoding in a serial-temporal format, optical time-stretch imaging entails a stringent requirement of state-of-the-art fast data acquisition unit in order to preserve high image resolution at an ultrahigh frame rate - hampering the widespread utilities of such technology. Here, we propose a pixel super-resolution (pixel-SR) technique tailored for time-stretch imaging that preserves pixel resolution at a relaxed sampling rate. It harnesses the subpixel shifts between image frames inherently introduced by asynchronous digital sampling of the continuous time-stretch imaging process. Precise pixel registration is thus accomplished without any active opto-mechanical subpixel-shift control or other additional hardware. Here, we present the experimental pixel-SR image reconstruction pipeline that restores high-resolution time-stretch images of microparticles and biological cells (phytoplankton) at a relaxed sampling rate (≈2-5 GSa/s)-more than four times lower than the originally required readout rate (20 GSa/s) - is thus effective for high-throughput label-free, morphology-based cellular classification down to single-cell precision. Upon integration with the high-throughput image processing technology, this pixel-SR time-stretch imaging technique represents a cost-effective and practical solution for large scale cell-based phenotypic screening in biomedical diagnosis and machine vision for quality control in manufacturing.

Ultrafast laser-scanning time-stretch imaging at visible wavelengths

Optical time-stretch imaging enables the continuous capture of non-repetitive events in real time at a line-scan rate of tens of MHz-a distinct advantage for the ultrafast dynamics monitoring and high-throughput screening that are widely needed in biological microscopy. However, its potential is limited by the technical challenge of achieving significant pulse stretching (that is, high temporal dispersion) and low optical loss, which are the critical factors influencing imaging quality, in the visible spectrum demanded in many of these applications. We present a new pulse-stretching technique, termed free-space angular-chirp-enhanced delay (FACED), with three distinguishing features absent in the prevailing dispersive-fiber-based implementations: (1) it generates substantial, reconfigurable temporal dispersion in free space (>1 ns nm-1) with low intrinsic loss (<6 dB) at visible wavelengths; (2) its wavelength-invariant pulse-stretching operation introduces a new paradigm in time-stretch imaging, which can now be implemented both with and without spectral encoding; and (3) pulse stretching in FACED inherently provides an ultrafast all-optical laser-beam scanning mechanism at a line-scan rate of tens of MHz. Using FACED, we demonstrate not only ultrafast laser-scanning time-stretch imaging with superior bright-field image quality compared with previous work but also, for the first time, MHz fluorescence and colorized time-stretch microscopy. Our results show that this technique could enable a wider scope of applications in high-speed and high-throughput biological microscopy that were once out of reach.

Assessing the effects of riboflavin/UV-A crosslinking on porcine corneal mechanical anisotropy with optical coherence elastography

In this work we utilize optical coherence elastography (OCE) to assess the effects of UV-A/riboflavin corneal collagen crosslinking (CXL) on the mechanical anisotropy of in situ porcine corneas at various intraocular pressures (IOP). There was a distinct meridian of increased Young's modulus in all samples, and the mechanical anisotropy increased as a function of IOP and also after CXL. The presented noncontact OCE technique was able to quantify the Young's modulus and elastic anisotropy of the cornea and their changes as a function of IOP and CXL, opening new avenues of research for evaluating the effects of CXL on corneal biomechanical properties.

Gain-guided soliton fiber laser with high-quality rectangle spectrum for ultrafast time-stretch microscopy

The ultrafast time-stretch microscopy has been proposed to enhance the temporal resolution of a microscopy system. The optical source is a key component for ultrafast time-stretch microscopy system. Herein, we reported on the gain-guided soliton fiber laser with high-quality rectangle spectrum for ultrafast time-stretch microscopy. By virtue of the excellent characteristics of the gain-guided soliton, the output power and the 3-dB bandwidth of the stable mode-locked soliton could be up to 3 mW and 33.7 nm with a high-quality rectangle shape, respectively. With the proposed robust optical source, the ultrafast time-stretch microscopy with the 49.6 μm resolution and a scan rate of 11 MHz was achieved without the external optical amplification. The obtained results demonstrated that the gain-guided soliton fiber laser could be used as an alternative high-quality optical source for ultrafast time-stretch microscopy and will introduce some applications in fields such as biology, chemical, and optical sensing.

Optofluidic time-stretch imaging - an emerging tool for high-throughput imaging flow cytometry

Optical imaging is arguably the most effective tool to visualize living cells with high spatiotemporal resolution and in a nearly noninvasive manner. Driven by this capability, state-of-the-art cellular assay techniques have increasingly been adopting optical imaging for classifying different cell types/stages, and thus dissecting the respective cellular functions. However, it is still a daunting task to image and characterize cell-to-cell variability within an enormous and heterogeneous population - an unmet need in single-cell analysis, which is now widely advocated in modern biology and clinical diagnostics. The challenge stems from the fact that current optical imaging technologies still lack the practical speed and sensitivity for measuring thousands to millions of cells down to the single-cell precision. Adopting the wisdom in high-speed fiber-optics communication, optical time-stretch imaging has emerged as a completely new optical imaging concept which is now proven for ultrahigh-throughput optofluidic single-cell imaging, at least 1-2 orders-of-magnitude higher (up to ∼100 000 cells per second) compared to the existing imaging flow cytometers. It also uniquely enables quantification of intrinsic biophysical markers of individual cells - a largely unexploited class of single-cell signatures that is known to be correlated with the overwhelmingly investigated biochemical markers. With the aim of reaching a wider spectrum of experts specializing in cellular assay developments and applications, this paper highlights the essential basics of optical time-stretch imaging, followed by reviewing the recent developments and applications of optofluidic time-stretch imaging. We will also discuss the current challenges of this technology, in terms of providing new insights in basic biology and enriching the clinical diagnostic toolsets.

Compact and stable temporally magnified tomography using a phase-locked broadband source

The temporally magnified tomography system is further improved in terms of resolution and imaging stability. We simplify the system configuration and improve the axial resolution simultaneously by utilizing a stabilized all-fiber broadband source. The highly stable spectrum of the source assisted by a phase-locked loop guarantees an improved imaging quality. In addition, the impact of the repetition-rate fluctuation of the source to the system stability is analyzed, which also applies to other temporal imaging systems. Achieving a 90-μm in-air resolution at 89-MHz A-scan rate and improved stability, we are taking one major step toward the practical application of this new optical tomographic modality.

Deep Learning in Label-free Cell Classification

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.