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Single-frame Label-free Cell Tomography for High Throughput 3D Image Cytometry Applications
Quantitative phase imaging (QPI) is a label-free imaging technique that has been widely applied to biomedical imaging and material metrology. We have recently empowered QPI with artificial intelligence (AI) and high three-dimensional (3D) imaging speed to realize high-throughput single cell analysis with a high accuracy. For this endeavor, we developed SIngle-frame Label-free Cell Tomography (SILACT) that can achieve diffraction-limited spatial resolution and sub-millisecond temporal resolution. SILACT is realized through training a deep neural network (DNN) in an angle-multiplexed optical diffraction tomography (ODT) system to reconstruct the 3D refractive index maps of cells. Cells of various types are reconstructed in 3D using this method and the results are validated with a beam propagation-based reconstruction method. We applied this new imaging method for observing 3D red blood cell deformations in microfluidic channels and demonstrating 3D image flow cytometry at a throughput of over 10,000 cells/second. We will present these progresses and highlight their potential applications.
Dr. Renjie Zhou is an Assistant Professor in the Department of Biomedical Engineering at The Chinese University of Hong Kong. He directs the Laser Metrology and Biomedicine Laboratory which he founded in 2017. Dr. Zhou received his doctoral degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2014 and undertook postdoctoral training at the George R. Harrison Spectroscopy Lab at MIT from 2014-2017. His current research interest is in developing optical precision instruments for material metrology and biomedical imaging applications. He has published over 40 journal papers and filed 5 US patent applications.
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