Ata Mahjoubfar

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.

Hybrid Dispersion Laser Scanner

Laser scanning technology is one of the most integral parts of todays scientific research, manufacturing, defense, and biomedicine. In many applications, high-speed scanning capability is essential for scanning a large area in a short time and multi-dimensional sensing of moving objects and dynamical processes with fine temporal resolution. Unfortunately, conventional laser scanners are often too slow, resulting in limited precision and utility. Here we present a new type of laser scanner that offers ∼1,000 times higher scan rates than conventional state-of-the-art scanners. This method employs spatial dispersion of temporally stretched broadband optical pulses onto the target, enabling inertia-free laser scans at unprecedented scan rates of nearly 100 MHz at 800 nm. To show our scanners broad utility, we use it to demonstrate unique and previously difficult-to-achieve capabilities in imaging, surface vibrometry, and flow cytometry at a record 2D raster scan rate of more than 100 kHz with 27,000 resolvable points.

Label-free high-throughput cell screening in flow.

Flow cytometry is a powerful tool for cell counting and biomarker detection in biotechnology and medicine especially with regards to blood analysis. Standard flow cytometers perform cell type classification both by estimating size and granularity of cells using forward- and side-scattered light signals and through the collection of emission spectra of fluorescently-labeled cells. However, cell surface labeling as a means of marking cells is often undesirable as many reagents negatively impact cellular viability or provide activating/inhibitory signals, which can alter the behavior of the desired cellular subtypes for downstream applications or analysis. To eliminate the need for labeling, we introduce a label-free imaging-based flow cytometer that measures size and cell protein concentration simultaneously either as a stand-alone instrument or as an add-on to conventional flow cytometers. Cell protein concentration adds a parameter to cell classification, which improves the specificity and sensitivity of flow cytometers without the requirement of cell labeling. This system uses coherent dispersive Fourier transform to perform phase imaging at flow speeds as high as a few meters per second.

High-speed nanometer-resolved imaging vibrometer and velocimeter

Conventional laser vibrometers are incapable of performing multidimensional vibrometry at high speeds because they build on single-point measurements and rely on beam scanning, significantly limiting their utility and precision. Here we introduce a laser vibrometer that performs high-speed multidimensional imaging-based vibration and velocity measurements with nanometer-scale axial resolution without the need for beam scanning. As a proof-of-concept, we demonstrate real-time microscopic imaging of acoustic vibrations with 1 nm axial resolution, 1200 image pixels, and 30 ps dwell time at 36.7 MHz scan rate.

Optical Data Compression in Time Stretch Imaging

Time stretch imaging offers real-time image acquisition at millions of frames per second and subnanosecond shutter speed, and has enabled detection of rare cancer cells in blood with record throughput and specificity. An unintended consequence of high throughput image acquisition is the massive amount of digital data generated by the instrument. Here we report the first experimental demonstration of real-time optical image compression applied to time stretch imaging. By exploiting the sparsity of the image, we reduce the number of samples and the amount of data generated by the time stretch camera in our proof-of-concept experiments by about three times. Optical data compression addresses the big data predicament in such systems.

Nomarski serial time-encoded amplified microscopy for high-speed contrast-enhanced imaging of transparent media

High-speed high-contrast imaging modalities that enable image acquisition of transparent media without the need for chemical staining are essential tools for a broad range of applications; from semiconductor process monitoring to blood screening. Here we introduce a method for contrast-enhanced imaging of unstained transparent objects that is capable of high-throughput imaging. This method combines the Nomarski phase contrast capability with the ultrahigh frame rate and shutter speed of serial time-encoded amplified microscopy. As a proof of concept, we show imaging of a transparent test structure and white blood cells in flow at a shutter speed of 33 ps and a frame rate of 36.1 MHz using a single-pixel photo-detector. This method is expected to be a valuable tool for high-throughput screening of unstained cells.

Ultrafast dark-field surface inspection with hybrid-dispersion laser scanning

High-speed surface inspection plays an important role in industrial manufacturing, safety monitoring, and quality control. It is desirable to go beyond the speed limitation of current technologies for reducing manufacturing costs and opening a new window onto a class of applications that require high-throughput sensing. Here, we report a high-speed dark-field surface inspector for detection of micrometer-sized surface defects that can travel at a record high speed as high as a few kilometers per second. This method is based on a modified time-stretch microscope that illuminates temporally and spatially dispersed laser pulses on the surface of a fast-moving object and detects scattered light from defects on the surface with a sensitive photodetector in a dark-field configuration. The inspectors ability to perform ultrafast dark-field surface inspection enables real-time identification of difficult-to-detect features on weakly reflecting surfaces and hence renders the method much more practical than in the...

Tailoring Wideband Signals With a Photonic Hardware Accelerator

Digital hardware accelerators are increasingly employed to speed up computation and reduce power dissipation, enabling real-time operation. Inspired by this paradigm, we propose the concept of the photonic hardware accelerator-an analog optical processing engine that precedes optical-to-electrical conversion and alleviates the burden on subsequent electronics. We propose one specific class of photonic hardware accelerators designed to assist in acquisition, feature extraction, and storage of wideband waveforms. This fundamental unit reshapes, in real time, the spectrotemporal evolution of a wideband streaming signal based on signals sparsity. Functioning as an information gearbox, the accelerator transforms the signal according to the nonuniform entropy of its spectrum. Nonlinear group delay dispersion modes are introduced as primitive building blocks for such transformations. Representing spectrotemporal basis functions, these modes and their corresponding time-stretch wavelets have distinct and useful properties that depend on their symmetry. We focus on polynomial basis functions, but also discuss their limitations and alternatives such as spline functions that offer more efficient representation of group delay spectra with localized features. They are used to synthesize complex warped spectrotemporal operations that are reconfigurable and can be implemented in both analog optical and digital (computational) domains. We show how these dispersion-based computational primitives reshape the wideband signal to enable nonuniform sampling, compression, and pattern recognition in real time. Additional applications including coding, signal classification, and enhancement of signal-to-noise ratio during ultrafast analog-to-digital conversion are discussed.

Optically amplified detection for biomedical sensing and imaging

Optical sensing and imaging methods for biomedical applications, such as spectroscopy and laser-scanning fluorescence microscopy, are incapable of performing sensitive detection at high scan rates due to the fundamental trade-off between sensitivity and speed. This is because fewer photons are detected during short integration times and hence the signal falls below the detector noise. Optical postamplification can, however, overcome this challenge by amplifying the collected optical signal after collection and before photodetection. Here we present a theoretical analysis of the sensitivity of high-speed biomedical sensing and imaging systems enhanced by optical postamplifiers. As a case study, we focus on Raman amplifiers because they produce gain at any wavelength within the gain mediums transparency window and are hence suitable for biomedical applications. Our analytical model shows that when limited by detector noise, such optically postamplified systems can achieve a sensitivity improvement of up to 20 dB in the visible to near-infrared spectral range without sacrificing speed. This analysis is expected to be valuable for design of fast real-time biomedical sensing and imaging systems.

Design of Warped Stretch Transform.

Time stretch dispersive Fourier transform enables real-time spectroscopy at the repetition rate of million scans per second. High-speed real-time instruments ranging from analog-to-digital converters to cameras and single-shot rare-phenomena capture equipment with record performance have been empowered by it. Its warped stretch variant, realized with nonlinear group delay dispersion, offers variable-rate spectral domain sampling, as well as the ability to engineer the time-bandwidth product of the signal’s envelope to match that of the data acquisition systems. To be able to reconstruct the signal with low loss, the spectrotemporal distribution of the signal spectrum needs to be sparse. Here, for the first time, we show how to design the kernel of the transform and specifically, the nonlinear group delay profile dictated by the signal sparsity. Such a kernel leads to smart stretching with nonuniform spectral resolution, having direct utility in improvement of data acquisition rate, real-time data compression, and enhancement of ultrafast data capture accuracy. We also discuss the application of warped stretch transform in spectrotemporal analysis of continuous-time signals.