PyFLI：用于荧光寿命成像数据生成、参数估计和基准测试的统一Python框架

中文摘要 AI 辅助

荧光寿命成像（FLI）测量激发后荧光的随时间衰减，并提供关于荧光团局部环境和分子相互作用的定量信息。根据荧光团和实验设计的不同，寿命可以报告与pH值、氧合、细胞代谢和福斯特共振能量转移相关的变化。这些特性使FLI在显微镜、生物物理学、生物医学光学和临床前成像中非常有用，在这些领域中，可以在不同的生物尺度上研究相同类型的分子对比度。然而，FLI测量是通过以不同方式记录荧光的仪器获取的。增强型电荷耦合器件（ICCD）相机、单光子雪崩二极管（SPAD）阵列和时间相关单光子计数（TCSPC）系统在时间采样、数据组织、探测器噪声、仪器响应和文件格式方面存在差异。寿命估计方法也对记录的衰减做出不同的假设，而基于学习的方法需要真实且已知底层参数的训练和验证数据。PyFLI是一个用于FLI处理和标准化数据模拟的开源框架。它从采集系统导入测量数据，在可配置的采集和噪声条件下模拟标记数据，并提供互补的参数估计方法。这些方法包括非线性最小二乘拟合（NLSF）、最大似然估计（MLE）、相量分析、快速寿命测定（RLD）、基于拉盖尔多项式的估计以及可选的贝叶斯和深度学习推理。CPU和GPU处理支持图像尺度分析，而重建、可视化、统计分析和跨软件比较为评估结果提供了工具。PyFLI包括用于单像素高光谱FLI的压缩感知重建。

英文摘要

Fluorescence lifetime imaging (FLI) measures the temporal decay of fluorescence after excitation and provides quantitative information about a fluorophore's local environment and molecular interactions. Depending on the fluorophore and experimental design, lifetime can report changes associated with pH, oxygenation, cellular metabolism, and Forster resonance energy transfer. These properties make FLI useful across microscopy, biophysics, biomedical optics, and preclinical imaging, where the same type of molecular contrast can be studied across different biological scales. FLI measurements, however, are acquired with instruments that record fluorescence in different ways. Intensified charge-coupled device (ICCD) cameras, single-photon avalanche diode (SPAD) arrays, and time-correlated single-photon counting (TCSPC) systems differ in temporal sampling, data organization, detector noise, instrument response, and file format. Lifetime-estimation methods also make different assumptions about the recorded decay, while learning-based approaches require realistic training and validation data for which the underlying parameters are known. PyFLI is an open-source framework for FLI processing and standardized data simulation. It imports measurements from acquisition systems, simulates labeled data under configurable acquisition and noise conditions, and provides complementary approaches for parameter estimation. These include nonlinear least-squares fitting (NLSF), maximum-likelihood estimation (MLE), phasor analysis, rapid lifetime determination (RLD), Laguerre-based estimation, and optional Bayesian and deep-learning inference. CPU and GPU processing support image-scale analysis, while reconstruction, visualization, statistical analysis, and cross-software comparison provide tools for evaluating results. PyFLI includes compressed-sensing reconstruction for single-pixel hyperspectral FLI.

发表机构

• Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute（医学建模、模拟与成像中心，伦斯勒理工学院）
• Department of Molecular and Cellular Physiology, Albany Medical College（分子与细胞生理学系，奥尔巴尼医学院）

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