High-throughput Biological Cell Classification Featuring Real-time Optical Data Compression

TL;DR

This paper discusses a high-throughput optical data compression technique for real-time biological cell classification, leveraging warped stretch transformation to handle large data streams from photonic time stretch instruments.

Contribution

Introduction of a novel optical compression method based on warped stretch transformation for real-time data handling in high-speed biological imaging.

Findings

Achieved real-time optical data compression at 100 million frames per second

Enabled detection of rare cancer cells with high sensitivity

Demonstrated effective data reduction for large biological datasets

Abstract

High throughput real-time instruments are needed to acquire large data sets for detection and classification of rare events. Enabled by the photonic time stretch digitizer, a new class of instruments with record throughputs have led to the discovery of optical rogue waves [1], detection of rare cancer cells [2], and the highest analog-to-digital conversion performance ever achieved [3]. Featuring continuous operation at 100 million frames per second and shutter speed of less than a nanosecond, the time stretch camera is ideally suited for screening of blood and other biological samples. It has enabled detection of breast cancer cells in blood with record, one-in-a-million, sensitivity [2]. Owing to their high real-time throughput, instruments produce a torrent of data - equivalent to several 4K movies per second - that overwhelm data acquisition, storage, and processing operations. This predicament calls for technologies that compress images in optical domain and in real-time. An example of this, based on warped stretch transformation and non-uniform Fourier domain sampling will be reported.