Fluorescence lifetime imaging (FLIM) allows the quantification of sub-cellular processes in situ, in living cells. A number of approaches have been developed to extract the lifetime from time-domain FLIM data, but they are often limited in terms of speed, photon efficiency, precision or the dynamic range of lifetimes they can measure. Here, we focus on one of the best performing methods in the field, the centre-of-mass method (CMM), that conveys advantages in terms of speed and photon efficiency over others. In this paper, however, we identify a loss of photon efficiency of CMM for short lifetimes when background noise is present. We subsequently present a new development and generalization of CMM that provides for the rapid and accurate extraction of fluorescence lifetime over a large lifetime dynamic range. We provide software tools to simulate, validate and analyse FLIM data sets and compare the performance of our approach against the standard CMM and the commonly employed least-square minimization (LSM) methods. Our method features a better photon efficiency than standard CMM and LSM and is robust in the presence of background noise. The algorithm is applicable to any time-domain FLIM data set.

中文翻译：

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一种在大动态范围内对时域荧光寿命图像数据进行快速和光子高效分析的方法

荧光寿命成像 (FLIM) 允许在活细胞中原位量化亚细胞过程。已经开发了许多方法来从时域 FLIM 数据中提取寿命，但它们通常在速度、光子效率、精度或它们可以测量的寿命动态范围方面受到限制。在这里，我们专注于该领域表现最好的方法之一，即质心法 (CMM)，它在速度和光子效率方面优于其他方法。然而，在本文中，我们发现当存在背景噪声时，CMM 的光子效率会在短寿命内损失。我们随后提出了 CMM 的新发展和推广，它提供了在大寿命动态范围内快速准确地提取荧光寿命。我们提供软件工具来模拟，验证和分析 FLIM 数据集，并将我们的方法的性能与标准 CMM 和常用的最小二乘最小化 (LSM) 方法进行比较。我们的方法具有比标准 CMM 和 LSM 更好的光子效率，并且在存在背景噪声的情况下具有鲁棒性。该算法适用于任何时域 FLIM 数据集。