Diffuse correlation spectroscopy (DCS) is increasingly used in the optical imaging field to assess blood flow in humans due to its non-invasive, real-time characteristics and its ability to provide label-free, bedside monitoring of blood flow changes. Previous DCS studies have utilized a traditional curve fitting of the analytical or Monte Carlo models to extract the blood flow changes, which are computationally demanding and less accurate when the signal to noise ratio decreases. Here, we present a deep learning model that eliminates this bottleneck by solving the inverse problem more than 2300% faster, with equivalent or improved accuracy compared to the nonlinear fitting with an analytical method. The proposed deep learning inverse model will enable real-time and accurate tissue blood flow quantification with the DCS technique.

中文翻译：

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深度学习模型，用于弥散相关光谱法中的血流超快速定量

扩散相关光谱法（DCS）由于其非侵入性的实时特性以及提供无标签的床旁血流变化监测功能，因此在光学成像领域越来越多地用于评估人的血流。先前的DCS研究已经利用分析或蒙特卡洛模型的传统曲线拟合来提取血流变化，当信噪比降低时，这在计算上要求很高，并且准确性较差。在这里，我们提出了一种深度学习模型，该模型可通过更快地解决2300％以上的逆问题来消除此瓶颈，与使用解析方法进行非线性拟合相比，具有同等或更高的精度。提出的深度学习逆模型将通过DCS技术实现实时，准确的组织血流定量。