The effect of bit number and sampling rate of a digitizer on least-square multi exponential decay analysis in fluorescence lifetime imaging

2017 ◽  
Author(s):  
Won Sang Hwang ◽  
Dong Eun Kim ◽  
Jun Woo Kim ◽  
Youn Young Ji ◽  
Byung Hwy So ◽  
...  
Keyword(s):  
Exponential Decay ◽  
Fluorescence Lifetime ◽  
Sampling Rate ◽  
Least Square ◽  
Fluorescence Lifetime Imaging ◽  
Lifetime Imaging

scholarly journals Timing and Operating Mode Design for Time-Gated Fluorescence Lifetime Imaging Microscopy

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Chao Liu ◽  
Xinwei Wang ◽  
Yan Zhou ◽  
Yuliang Liu
Keyword(s):  
High Resolution ◽  
Fluorescence Imaging ◽  
Fluorescence Lifetime ◽  
Imaging Techniques ◽  
Absolute Measurement ◽  
Operating Mode ◽  
Least Square ◽  
Fluorescence Lifetime Imaging Microscopy ◽  
Fluorescence Lifetime Imaging ◽  
Lifetime Imaging

Steady-state fluorence imaging and time-resolved fluorescence imaging are two important areas in fluorescence imaging research. Fluorescence lifetime imaging is an absolute measurement method which is independent of excitation laser intensity, fluorophore concentration, and photobleaching compared to fluorescence intensity imaging techniques. Time-gated fluorescence lifetime imaging microscopy (FLIM) can provide high resolution and high imaging frame during mature FLIM methods. An abstract time-gated FLIM model was given, and important temporal parameters are shown as well. Aiming at different applications of steady and transient fluorescence processes, two different operation modes, timing and lifetime computing algorithm are designed. High resolution and high frame can be achieved by one-excitation one-sampling mode and least square algorithm for steady imaging applications. Correspondingly, one-excitation two-sampling mode and rapid lifetime determination algorithm contribute to transient fluorescence situations.

scholarly journals Deep learning enables rapid and robust analysis of fluorescence lifetime imaging in photon-starved conditions

2020 ◽  
Author(s):  
Yuan-I Chen ◽  
Yin-Jui Chang ◽  
Shih-Chu Liao ◽  
Trung Duc Nguyen ◽  
Jianchen Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Lifetime ◽  
Resonance Energy ◽  
Excitation Power ◽  
Least Square ◽  
Biological Research ◽  
Fluorescence Lifetime Imaging ◽  
Accurate Analysis ◽  
Lifetime Imaging ◽  
Cellular Environment

AbstractFluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study the molecular states in the complex cellular environment as the lifetime readings are not biased by the fluorophore concentration or the excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is not only 258 times faster than the most popular time-domain least-square estimation (TD_LSE) method but also provide more accurate analysis in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where ultrafast analysis is critical.

Analysis of Fluorescence Lifetime Imaging Microscopy (FLIM) Data

2010 ◽  
Vol 49 (05) ◽  
pp. 531-536 ◽  
Author(s):  
P. Pande ◽  
C. A. Trivedi ◽  
J. A. Jo
Keyword(s):  
Fluorescence Lifetime ◽  
Fluorescent Dyes ◽  
Simulated Data ◽  
Nonlinear Least Squares ◽  
Least Square ◽  
Fluorescence Lifetime Imaging Microscopy ◽  
Fluorescence Lifetime Imaging ◽  
Deconvolution Method ◽  
Lifetime Imaging ◽  
Tissue Fluorescence

Summary Objectives: A novel Fluorescence Lifetime Imaging Microscopy (FLIM) deconvolution method based on the linear expansion of fluorescence decays on a set of orthonormal Laguerre functions was recently proposed. The Laguerre deconvolution method applies linear least-square estimation to estimate the expansion coefficients of all pixel decays simultaneously, performing at least two orders of magnitude faster than the other algorithms. In the original Laguerre FLIM deconvolution implementation, however, the Laguerre parameter α is selected using a heuristic approach, making it unsuitable for online applications. Methods: In this study, we present a fully automated implementation of the Laguerre FLIM deconvolution, whereby the Laguerre parameter α is treated as a free parameter within a nonlinear least-squares optimization scheme. Results: The performance of this method has been successfully validated on simulated data, and experimental FLIM images of standard fluorescent dyes and endogenous tissue fluorescence. Conclusions: The main advantage of the proposed method is that it does not require any user intervention for tuning up the deconvolution process. Thus, we believe this method will facilitate the translation of FLIM to online applications, including real-time clinical diagnosis.

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