Virtual optofluidic time-stretch quantitative phase imaging
Virtual optofluidic time-stretch quantitative phase imaging
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DOI：
10.1063/1.5134125
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发表时间：
2020-04-01
期刊：
影响因子：
5.6
通讯作者：
Goda, Keisuke
中科院分区：
文献类型：
--
作者：
Yan, Haochen;Wu, Yunzhao;Goda, Keisuke
光流体时间拉伸定量相位成像（OTS-QPI）是生物医学应用的有力工具，因为它能够以无标记方式对大量细胞进行高通量QPI，用于大规模单细胞分析。然而，有一些关键的限制，阻碍OTS-QPI被广泛应用于不同的应用，如昂贵的仪器和固有的相位展开误差。在这里，为了克服这些局限性，我们提出了一种无QPI的OTS-QPI方法，该方法通过使用大量亮场和相位图像对训练的深度神经网络，从其相应的亮场图像生成“虚拟”相位图像。具体来说，我们训练的生成对抗网络模型生成的虚拟相位图像与其对应的真实的相位图像具有高度相似性（结构相似性指数>0.7）。这也得到了我们通过其虚拟相位图像对各种类型的白血病细胞和白色血细胞的成功分类的支持。虚拟OTS-QPI方法具有高度可靠性和成本效益，因此有望增强OTS显微镜在癌症生物学、精密医学和绿色能源等不同研究领域的适用性。
Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a potent tool for biomedical applications as it enables high-throughput QPI of numerous cells for large-scale single-cell analysis in a label-free manner. However, there are a few critical limitations that hinder OTS-QPI from being widely applied to diverse applications, such as its costly instrumentation and inherent phase-unwrapping errors. Here, to overcome the limitations, we present a QPI-free OTS-QPI method that generates "virtual" phase images from their corresponding bright-field images by using a deep neural network trained with numerous pairs of bright-field and phase images. Specifically, our trained generative adversarial network model generated virtual phase images with high similarity (structural similarity index >0.7) to their corresponding real phase images. This was also supported by our successful classification of various types of leukemia cells and white blood cells via their virtual phase images. The virtual OTS-QPI method is highly reliable and cost-effective and is therefore expected to enhance the applicability of OTS microscopy in diverse research areas, such as cancer biology, precision medicine, and green energy.