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Über dieses Buch

This book introduces time-stretch quantitative phase imaging (TS-QPI), a high-throughput label-free imaging flow cytometer developed for big data acquisition and analysis in phenotypic screening. TS-QPI is able to capture quantitative optical phase and intensity images simultaneously, enabling high-content cell analysis, cancer diagnostics, personalized genomics, and drug development. The authors also demonstrate a complete machine learning pipeline that performs optical phase measurement, image processing, feature extraction, and classification, enabling high-throughput quantitative imaging that achieves record high accuracy in label -free cellular phenotypic screening and opens up a new path to data-driven diagnosis.



Time Stretch Imaging


Chapter 1. Introduction

This chapter provides an overview of the book and briefly describes the objectives of each section.

Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali

Chapter 2. Time Stretch

Time stretch is the leading technology in ultrafast big-data acquisition. Here we introduce time stretch technique and highlight its applications in the context of imaging.

Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali

Inspection and Vision


Chapter 3. Nanometer-Resolved Imaging Vibrometer

Conventional laser vibrometers are incapable of performing multi-dimensional 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 multi-dimensional 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.

Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali

Chapter 4. Three-Dimensional Ultrafast Laser Scanner

Laser scanners are essential for scientific research, manufacturing, defense, and medical practice. Unfortunately, often times the speed of conventional laser scanners (e.g., galvanometric mirrors and acousto-optic deflectors) falls short for many applications, resulting in motion blur and failure to capture fast transient information. Here, we present a novel type of laser scanner that offers roughly three orders of magnitude higher scan rates than conventional methods. Our laser scanner, which we refer to as the hybrid dispersion laser scanner, performs inertia-free laser scanning by dispersing a train of broadband pulses both temporally and spatially. More specifically, each broadband pulse is temporally processed by time stretch dispersive Fourier transform and further dispersed into space by one or more diffractive elements such as prisms and gratings. As a proof-of-principle demonstration, we perform 1D line scans at a record high scan rate of 91 MHz and 2D raster scans and 3D volumetric scans at an unprecedented scan rate of 105 kHz. The method holds promise for a broad range of scientific, industrial, and biomedical applications. To show the utility of our method, we demonstrate imaging, nanometer-resolved surface vibrometry, and high-precision flow cytometry with real-time throughput that conventional laser scanners cannot offer due to their low scan rates.

Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali

Biomedical Applications


Chapter 5. Label-Free High-Throughput Phenotypic Screening

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.

Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali

Chapter 6. Time Stretch Quantitative Phase Imaging

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.

Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali

Big Data and Artificial Intelligence


Chapter 7. Big Data Acquisition and Processing in Real-Time

Coherent-STEAM is a quantitative phase microscopy technique for label-free analysis of up to 100,000 cells per second in flow. Here, we introduce a data acquisition scheme that enables interruptionless storage of Coherent-STEAM cell images. Our proof of principle demonstration is capable of saving 10.8 TB of cell images in an hour, i.e., pictures of every single cell in 2.7 mL of a sample.

Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali

Chapter 8. Deep Learning and Classification

As demonstrated in previous chapters, our TS-QPI system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. In this chapter, we use these biophysical measurements to form a hyperdimensional feature space in which supervised learning is performed for cell classification. We show that TS-QPI not only overcomes the throughput issue in cellular imaging, but also improves label-free diagnosis by integration of sensing multiple biophysical features. We also 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.

Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali

Data Compression


Chapter 9. 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.

Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali

Chapter 10. 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.

Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali

Chapter 11. Concluding Remarks and Future Work

In this chapter, we conclude the book and suggest future trends in time stretch imaging.

Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali


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