Referencing techniques for the analog mean-delay method in fluorescence lifetime imaging

Young Jae Won, Sucbei Moon, Won Taek Han, Dug Young Kim

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

The analog mean-delay (AMD) method is a new powerful alternative method in determining the lifetime of a fluorescence molecule for high-speed confocal fluorescence lifetime imaging microscopy. Even though the photon economy and the lifetime precision of the AMD method are proven to be as good as those of the state-ofthe- art time-correlated single photon counting method, there have been some speculations and concerns about the accuracy of this method with respect to the absolute lifetime value of a fluorescence probe. In the AMD method, the temporal waveform of an emitted fluorescence signal is directly recorded with a slow digitizer whose bandwidth is much lower than the temporal resolution of the lifetime to be measured. We have found that the drifts and the fluctuations of the absolute zero position in a measured temporal waveform are the major problems in the AMD method. We have proposed electrical and optical referencing techniques that may suppress these errors. It is shown that there may exist more than 2 ns drift in a measured temporal waveform during the period of the first 12 min after electronic components are turned on. The standard deviation of a measured lifetime after this warm-up period can be as large as 51 ps without any referencing technique. We have shown that this error can be reduced to 9 ps with our electronic referencing technique. It is demonstrated that this can be further reduced to 4 ps by the optical referencing technique we have introduced.

Original languageEnglish
Pages (from-to)2402-2410
Number of pages9
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Volume27
Issue number11
DOIs
Publication statusPublished - 2010 Nov

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition

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