Lifetime determination algorithms for time-domain fluorescence lifetime imaging: a review

Yahui Li, Lixin Liu, Dong Xiao, Hang Li, Natakorn Sapermsap, Jinshou Tian, Yu Chen, David Day-Uei Li

Research output: Chapter in Book/Report/Conference proceedingChapter

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Abstract

Fluorescence lifetime imaging (FLIM) is powerful for monitoring cellular microenvironments, protein conformational changes, and protein interactions. It can facilitate metabolism research, drug screening, DNA sequencing, and cancer diagnosis. Lifetime determination algorithms (LDAs) adopted in FLIM analysis can influence biological interpretations and clinical diagnoses. Herein, we discuss the commonly used and advanced time-domain LDAs classified in fitting and non-fitting categories. The concept and explicit mathematical forms of LDAs are reviewed. The output lifetime parameter types are discussed, including lifetime components, average lifetimes, and graphic representation. We compare their performances, identify trends, and provide suggestions for end users in terms of multi-exponential decay unmixing ability, lifetime estimation precision, and processing speed.
Original languageEnglish
Title of host publicationFluorescence Imaging
Subtitle of host publicationRecent Advances and Applications
EditorsRaffaello Papadakis
Chapter5
Number of pages23
ISBN (Electronic)9781803551845, 9781803551852
DOIs
Publication statusPublished - 22 Nov 2023

Keywords

  • fluorescence lifetime imaging
  • lifetime determination algorithm
  • fitting method
  • non-fitting method
  • deep learning

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