FLIM data analysis based on Laguerre polynomial decomposition and machine-learning.

ABSTRACT: 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. We propose to extract the lifetimes and abundances directly from the decay traces through mach

SUBMITTER: Guo S 

PROVIDER: S-EPMC7790506 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature