Compressive high speed flow microscopy with motion contrast
(Conference Presentation)

Bryan Bosworth; Jasper R. Stroud; Dung N. Tran; Trac D. Tran; Sang Chin; Mark A. Foster

doi:10.1117/12.2216602

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28 June 2016 Compressive high speed flow microscopy with motion contrast (Conference Presentation)

Bryan Bosworth, Jasper R. Stroud, Dung N. Tran, Trac D. Tran, Sang Chin, Mark A. Foster

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Proceedings Volume 9720, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management; 97200Y (2016) https://doi.org/10.1117/12.2216602
Event: SPIE BiOS, 2016, San Francisco, California, United States

Abstract

High-speed continuous imaging systems are constrained by analog-to-digital conversion, storage, and transmission. However, real video signals of objects such as microscopic cells and particles require only a few percent or less of the full video bandwidth for high fidelity representation by modern compression algorithms. Compressed Sensing (CS) is a recent influential paradigm in signal processing that builds real-time compression into the acquisition step by computing inner products between the signal of interest and known random waveforms and then applying a nonlinear reconstruction algorithm. Here, we extend the continuous high-rate photonically-enabled compressed sensing (CHiRP-CS) framework to acquire motion contrast video of microscopic flowing objects. We employ chirp processing in optical fiber and high-speed electro-optic modulation to produce ultrashort pulses each with a unique pseudorandom binary sequence (PRBS) spectral pattern with 325 features per pulse at the full laser repetition rate (90 MHz). These PRBS-patterned pulses serve as random structured illumination inside a one-dimensional (1D) spatial disperser. By multiplexing the PRBS patterns with a user-defined repetition period, the difference signal y_i=phi_i (x_i - x_{i-tau}) can be computed optically with balanced detection, where x is the image signal, phi_i is the PRBS pattern, and tau is the repetition period of the patterns. Two-dimensional (2D) image reconstruction via iterative alternating minimization to find the best locally-sparse representation yields an image of the edges in the flow direction, corresponding to the spatial and temporal 1D derivative. This provides both a favorable representation for image segmentation and a sparser representation for many objects that can improve image compression.

Conference Presentation

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Bryan Bosworth, Jasper R. Stroud, Dung N. Tran, Trac D. Tran, Sang Chin, and Mark A. Foster "Compressive high speed flow microscopy with motion contrast (Conference Presentation)", Proc. SPIE 9720, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management, 97200Y (28 June 2016); https://doi.org/10.1117/12.2216602

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