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Related Concept Videos

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
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Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

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Related Experiment Video

Updated: Jun 2, 2026

Applications of Spatio-temporal Mapping and Particle Analysis Techniques to Quantify Intracellular Ca2+ Signaling In Situ
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Applications of Spatio-temporal Mapping and Particle Analysis Techniques to Quantify Intracellular Ca2+ Signaling In Situ

Published on: January 7, 2019

Upgrading time domain FLIM using an adaptive Monte Carlo data inflation algorithm.

Dave Trinel1, Aymeric Leray, Corentin Spriet

  • 1Interdisciplinary Research Institute, University of Lille - Nord de France.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|May 14, 2011
PubMed
Summary
This summary is machine-generated.

Adaptive Monte Carlo Data Inflation (AMDI) significantly reduces laser exposure times in Fluorescence Lifetime Imaging Microscopy (FLIM) by statistically enhancing photon data. This enables accurate lifetime estimation with up to 50x less exposure, preserving cellular health and improving spatial resolution.

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Last Updated: Jun 2, 2026

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Published on: January 7, 2019

Sample Drift Correction Following 4D Confocal Time-lapse Imaging
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Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

Area of Science:

  • Biophysics
  • Cell Biology
  • Microscopy

Background:

  • Fluorescence Lifetime Imaging Microscopy (FLIM) is crucial for studying cellular environments.
  • Standard FLIM requires extensive photon collection, leading to long exposure times.
  • Long exposures limit observation of dynamic events and cause cellular stress.

Purpose of the Study:

  • To develop a method to reduce FLIM exposure times.
  • To enable accurate fluorescence lifetime estimation with minimal photon counts.
  • To improve spatial resolution in FLIM imaging.

Main Methods:

  • Developed Adaptive Monte Carlo Data Inflation (AMDI).
  • AMDI combines bootstrap techniques with an adaptive Parzen kernel.
  • Validated using Monte Carlo simulations and live-cell imaging.

Main Results:

  • AMDI reduces exposure time by up to 50x for monoexponential decays (min. 20 photons/pixel).
  • AMDI reduces exposure time by up to 10x for biexponential decays (min. 5,000 photons/pixel).
  • Accurate parameter estimation achieved without constraints in Förster resonance energy transfer experiments.
  • Reduced spatial binning improved FLIM image resolution.

Conclusions:

  • AMDI offers a robust solution for minimizing FLIM exposure times.
  • The method allows for accurate fluorescence lifetime analysis in dynamic biological processes.
  • AMDI enhances the utility of FLIM for high-resolution cellular imaging.