Wu Yunzhao, Zhou Yuqi, Huang Chun-Jung, Kobayashi Hirofumi, Yan Sheng, Ozeki Yasuyuki, Wu Yingli, Sun Chia-Wei, Yasumoto Atsushi, Yatomi Yutaka, Lei Cheng, Goda Keisuke
Opt Express. 2020 Jan 6;28(1):519-532. doi: 10.1364/OE.380679.
Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a powerful tool as it enables high-throughput (>10,000 cell/s) QPI of single live cells. OTS-QPI is based on decoding temporally stretched spectral interferograms that carry the spatial profiles of cells flowing on a microfluidic chip. However, the utility of OTS-QPI is troubled by difficulties in phase retrieval from the high-frequency region of the temporal interferograms, such as phase-unwrapping errors, high instrumentation cost, and large data volume. To overcome these difficulties, we propose and experimentally demonstrate frequency-shifted OTS-QPI by bringing the phase information to the baseband region. Furthermore, to show its boosted utility, we use it to demonstrate image-based classification of leukemia cells with high accuracy over 96% and evaluation of drug-treated leukemia cells via deep learning.
光流控时间拉伸定量相位成像（OTS-QPI）是一种强大的工具，因为它能够对单个活细胞进行高通量（>10,000个细胞/秒）的定量相位成像。OTS-QPI基于对时间拉伸光谱干涉图的解码，这些干涉图携带了在微流控芯片上流动的细胞的空间轮廓。然而，OTS-QPI的实用性受到从时间干涉图的高频区域进行相位恢复时遇到的困难的困扰，例如相位展开误差、仪器成本高和数据量庞大。为了克服这些困难，我们提出并通过将相位信息带到基带区域来实验证明频移OTS-QPI。此外，为了展示其增强的实用性，我们使用它来高精度（超过96%）地证明白血病细胞的基于图像的分类，并通过深度学习评估药物处理的白血病细胞。
