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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2014)

High-throughput real-time optical sensing and imaging instruments for capture and analysis of fast phenomena are among the most essential tools for scientific, industrial, military, and most importantly biomedical applications. The key challenge in these instruments is the fundamental trade-off between speed and sensitivity of the measurement system due to the limited signal energy collected in each measurement window. Based on two enabling technologies, namely photonic time-stretch dispersive Fourier transform and optical amplification, we developed several novel high-throughput optical measurement tools for applications such as flow cytometry, vibrometry, and volumetric scanning.

We demonstrated optical Raman amplification at about 800 nm wavelength for the first time and extended time-stretch dispersive Fourier transform to this region of electromagnetic spectrum. We used this enabling technology to make an ultrafast three-dimensional laser scanner with about hundred thousand scans per second and an imaging vibrometer with nanometer-scale axial resolution.

We also employed our high-speed laser scanner to perform label-free cell screening in flow. One of the fundamental challenges in cell analysis is the undesirable impact of cell labeling on cellular behavior. To eliminate the need for these labels, while keeping the cell classification accuracy high, additional label-free parameters such as precise measurement of the cell protein concentration is required. We introduced a high-accuracy label-free imaging flow cytometer based on simultaneous measurement of morphology and optical path length through the cell at flow speeds as high as a few meters per second.

Finally, the ultimate challenge in ultra-high-throughput instrumentation is the storage and analysis of the torrent of generated data. As an example, our imaging flow cytometer generates about ten terabytes of cell images over a course of one hour acquisition, which captures images of every single cell in more than two milliliters of sample e.g. blood. We enabled practical use of these big data volumes by efficient combination of analog preprocessing techniques such as quadrature demodulation with parallel storage and digital post-processing.

    Cover page: High-Speed Imaging and Optical Sensing Systems for Biomedical Applications
    • Article
    • Peer Reviewed
    UCLA Previously Published Works (2011)
    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.
      Cover page: Nomarski serial time-encoded amplified microscopy for high-speed contrast-enhanced imaging of transparent media
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2019)
      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.
        Cover page: Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry
        • Article
        • Peer Reviewed
        Joint Genome Institute (2016)
        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.
          Cover page: Deep Learning in Label-free Cell Classification
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2012)
          Laser scanning technology is one of the most integral parts of today's 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 scanner's 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.
            Cover page: Hybrid Dispersion Laser Scanner
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