用于快速时域荧光寿命图像分析的直方图聚类

Histogram clustering for rapid time-domain fluorescence lifetime image analysis.

作者信息

Li Yahui, Sapermsap Natakorn, Yu Jun, Tian Jinshou, Chen Yu, Day-Uei Li David

机构信息

Key Laboratory of Ultra-fast Photoelectric Diagnostics Technology, Xi'an Institute of Optics and Precision Mechanics, Xi'an Shaanxi 710049, China.

Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan Shanxi 030006, China.

出版信息

Biomed Opt Express. 2021 Jun 21;12(7):4293-4307. doi: 10.1364/BOE.427532. eCollection 2021 Jul 1.

DOI:10.1364/BOE.427532

PMID:34457415

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8367240/

Abstract

We propose a histogram clustering (HC) method to accelerate fluorescence lifetime imaging (FLIM) analysis in pixel-wise and global fitting modes. The proposed method's principle was demonstrated, and the combinations of HC with traditional FLIM analysis were explained. We assessed HC methods with both simulated and experimental datasets. The results reveal that HC not only increases analysis speed (up to 106 times) but also enhances lifetime estimation accuracy. Fast lifetime analysis strategies were suggested with execution times around or below 30 s per histograms on MATLAB R2016a, 64-bit with the Intel Celeron CPU (2950M @ 2GHz).

摘要

我们提出了一种直方图聚类（HC）方法，以加速逐像素和全局拟合模式下的荧光寿命成像（FLIM）分析。阐述了该方法的原理，并解释了HC与传统FLIM分析的结合方式。我们使用模拟数据集和实验数据集对HC方法进行了评估。结果表明，HC不仅提高了分析速度（高达106倍），还提高了寿命估计的准确性。在配备英特尔赛扬CPU（2950M @ 2GHz）的64位MATLAB R2016a上，建议采用快速寿命分析策略，每个直方图的执行时间约为30秒或更短。