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Proceedings Paper

Imaging flow cytometer using computation and spatially coded filter
Author(s): Yuanyuan Han; Yu-Hwa Lo
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Paper Abstract

Flow cytometry analyzes multiple physical characteristics of a large population of single cells as cells flow in a fluid stream through an excitation light beam. Flow cytometers measure fluorescence and light scattering from which information about the biological and physical properties of individual cells are obtained. Although flow cytometers have massive statistical power due to their single cell resolution and high throughput, they produce no information about cell morphology or spatial resolution offered by microscopy, which is a much wanted feature missing in almost all flow cytometers. In this paper, we invent a method of spatial-temporal transformation to provide flow cytometers with cell imaging capabilities. The method uses mathematical algorithms and a specially designed spatial filter as the only hardware needed to give flow cytometers imaging capabilities. Instead of CCDs or any megapixel cameras found in any imaging systems, we obtain high quality image of fast moving cells in a flow cytometer using photomultiplier tube (PMT) detectors, thus obtaining high throughput in manners fully compatible with existing cytometers. In fact our approach can be applied to retrofit traditional flow cytometers to become imaging flow cytometers at a minimum cost. To prove the concept, we demonstrate cell imaging for cells travelling at a velocity of 0.2 m/s in a microfluidic channel, corresponding to a throughput of approximately 1,000 cells per second.

Paper Details

Date Published: 7 March 2016
PDF: 11 pages
Proc. SPIE 9720, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management, 972010 (7 March 2016); doi: 10.1117/12.2209401
Show Author Affiliations
Yuanyuan Han, Univ. of California, San Diego (United States)
Yu-Hwa Lo, Univ. of California, San Diego (United States)


Published in SPIE Proceedings Vol. 9720:
High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management
Kevin K. Tsia; Keisuke Goda, Editor(s)

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