Significance: The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis. Aim: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML). Approach: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker. Results: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components. Conclusions: The ML-based approach shows great performance in FLIM data analysis.

中文翻译：

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基于拉盖尔多项式分解和机器学习的 FLIM 数据分析

意义：荧光寿命成像显微镜 (FLIM) 的潜力最近得到了认可，尤其是在生物学研究中。然而，FLIM 并不直接测量寿命，而是记录荧光衰减痕迹。在数据处理阶段，必须根据这些轨迹估计寿命和/或丰度。精确估计这些参数具有挑战性，需要精心设计的计算机程序。传统采用的基于曲线拟合的方法计算成本高，性能有限，尤其是对于高噪声 FLIM 数据。图形分析虽然没有拟合，但需要校准样品进行定量分析。目的：我们建议通过机器学习（ML）直接从衰变轨迹中提取寿命和丰度。方法：基于 ML 的方法通过模拟测试数据进行了验证，其中准确地知道寿命和丰度。此后，我们根据时间相关单光子计数系统上的生物样本测量的数据集，将其性能与商业软件 SPCImage 进行了比较。我们使用 ML 和 SPCImage 方法估计的寿命和丰度值重建衰减轨迹，并利用均方根误差 (RMSE) 作为标记。结果：观察到 ML 的 RMSE（代表重建和测量的衰减轨迹之间的差异）低于 SPCImage。此外，我们可以通过三分量分析证明 ML 方法在处理两个以上生命周期分量方面的巨大潜力和灵活性。结论：