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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 developed.

Total Internal Reflection Fluorescence Microscopy

Total Internal Reflection Fluorescence Microscopy

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Updated: Jun 10, 2026

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Published on: July 17, 2012

Optimal Illumination Patterns for Fluorescence Tomography.

Joyita Dutta¹, Sangtae Ahn, Anand A Joshi

¹Signal and Image Processing Institute, Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, CA 90089, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|August 17, 2010

Summary

This summary is machine-generated.

This study optimizes illumination patterns for fluorescence tomography, enhancing in vivo molecular imaging in small animals. The new method maximizes data information content for clearer cellular process visualization.

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Area of Science:

Biomedical Imaging
Optical Imaging
Molecular Imaging

Background:

Fluorescence tomography is vital for in vivo molecular target detection and imaging cellular processes in small animals.
Acquiring data involves illuminating the animal with various excitation patterns to capture distinct fluorescence spatial patterns.

Purpose of the Study:

To solve the problem of optimally illuminating animal surfaces in fluorescence tomography.
To maximize information content in acquired data by optimizing illumination patterns.

Main Methods:

Parameterizing illumination patterns for fluorescence tomography.
Formulating the problem as a constrained optimization task.
Improving the conditioning of the Fisher information matrix to maximize data information content.

Main Results:

Developed an optimization approach for illumination patterns in fluorescence tomography.
Compared optimized geometric illumination schemes against standard methods using the Digimouse atlas.
Demonstrated improved information content through optimized illumination.

Conclusions:

The proposed optimization method effectively determines optimal illumination patterns for fluorescence tomography.
This approach enhances the quality and information content of in vivo molecular imaging data.
Further application of this method can advance small animal imaging studies.