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Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

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Related Experiment Video

Updated: Jul 6, 2025

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

Published on: January 18, 2017

Fast data fitting scheme for compressive multispectral fluorescence lifetime imaging.

Alberto Ghezzi, Andrea Farina, Vito Vurro

|January 9, 2024

Summary

This summary is machine-generated.

This study introduces a fast-fit workflow for single-pixel fluorescence microscopy, drastically cutting reconstruction time from minutes to under a second. This accelerates multidimensional data acquisition and analysis for advanced imaging applications.

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Last Updated: Jul 6, 2025

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

Published on: January 18, 2017

Fluorescence Lifetime Imaging of Molecular Rotors in Living Cells

Fluorescence Lifetime Imaging of Molecular Rotors in Living Cells

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Area of Science:

Optical microscopy
Computational imaging
Spectroscopy

Background:

Single-pixel cameras offer efficient multidimensional data acquisition (space, spectrum, lifetime) in fluorescence microscopy.
Compressive sensing (CS) reduces measurement time but requires lengthy post-acquisition computational reconstruction.
Existing reconstruction methods can negate CS benefits due to significant time demands.

Purpose of the Study:

To develop and validate a computationally efficient workflow for single-pixel fluorescence microscopy.
To significantly reduce the image reconstruction and data analysis time.
To enable parallel processing of data acquisition and analysis.

Main Methods:

Implementation of a fast-fit workflow utilizing global analysis.
Application of multiple linear fits for accelerated data processing.
Integration of the workflow with the measurement flow for parallel processing.

Main Results:

Computation time for image reconstruction and data analysis reduced from tens of minutes to under 1 second.
Experimental validation of the proposed fast-fit workflow.
Demonstration of parallel processing capabilities, interlaced with acquisition.

Conclusions:

The fast-fit workflow dramatically accelerates single-pixel fluorescence microscopy data processing.
This method overcomes the computational bottleneck of CS-based microscopy.
Enables faster and more efficient acquisition and analysis of multidimensional fluorescence data.