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.

中文翻译：

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虚拟光流时间拉伸定量相位成像

光电时延定量定量成像（OTS-QPI）是生物医学应用的有效工具，因为它可以无标记方式对众多细胞进行高通量QPI，以进行大规模单细胞分析。但是，有一些关键的限制因素阻碍了OTS-QPI广泛应用于各种应用程序，例如昂贵的仪器和固有的相位展开误差。在这里，为了克服局限性，我们提出了一种无QPI的OTS-QPI方法，该方法通过使用经过大量对明场和相位图像训练的深度神经网络，从其相应的明场图像生成“虚拟”相位图像。具体来说，我们训练有素的对抗网络模型生成的虚拟相位图像与其对应的真实相位图像具有高度相似性（结构相似性指数> 0.7）。我们通过其虚拟相位图像对各种类型的白血病细胞和白细胞进行了成功分类，这也为我们提供了支持。虚拟OTS-QPI方法高度可靠且具有成本效益，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。