By Topic

Automated Analysis of Fluorescence Lifetime Imaging Microscopy (FLIM) Data Based on the Laguerre Deconvolution Method

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Paritosh Pande ; Department of Biomedical Engineering , Texas A&M University, College Station, USA ; Javier A. Jo

In fluorescence lifetime imaging microscopy (FLIM), fluorescence time decay at each pixel of the imaged sample are measured. Every recorded fluorescence decay corresponds to the time convolution of the instrument response with the intrinsic fluorescence impulse response function (IRF), from which the sample fluorescence lifetime is determined. To estimate the IRF, the instrument response thus needs to be deconvolved from the recorded fluorescence decay. We have recently introduced a novel FLIM time-deconvolution method based on the linear expansion of the fluorescence decays on an orthonormal Laguerre basis. Since this method allows simultaneous estimation of the IRFs at all pixels, it performs at least two orders of magnitude faster than standard algorithms. In its original implementation, however, the Laguerre basis, determined by the Laguerre parameter , is selected using a heuristic approach. Here, we present an automated implementation, whereby the Laguerre parameter is treated as a free parameter within a nonlinear least squares optimization scheme. The new implementation combines the unmatched inherent computational speed of the Laguerre deconvolution method with a systematic model selection approach. This method will thus facilitate applications of FLIM requiring automatic estimation of the spatial distribution of fluorescence lifetimes, such as in in vivo tissue FLIM imaging.

Published in:

IEEE Transactions on Biomedical Engineering  (Volume:58 ,  Issue: 1 )