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Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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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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Total internal reflection fluorescence microscopy or TIRF is an advanced microscopic technique used to visualize fluorophores in samples close to a solid surface with a higher refractive index, such as a glass coverslip. TIRF only allows fluorophores in proximity to the solid surface to be excited. When light from a medium with a lower refractive index (such as air) hits the glass coverslip at a critical angle, the light undergoes total internal reflection stead of passing through the glass.
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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
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  11. Multierror Learning Enhanced Fluorescence Diffusion Tomography In Reflection Geometry

Multierror learning enhanced fluorescence diffusion tomography in reflection geometry

Haitao Chen, Kaixian Liu, Yuxuan Jiang

    Optics Express
    |June 14, 2025

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    View abstract on PubMed

    Summary
    This summary is machine-generated.

    We developed multierror-learning-enhanced fluorescence diffusion tomography in reflection geometry (MEL-rFDT) for precise 3D localization of deep intravital fluorescent probes. This method improves accuracy for neural circuit and tumor imaging applications.

    Area of Science:

    • Biomedical Optics
    • Medical Imaging
    • Neuroscience

    Background:

    • Accurate 3D localization of intravital fluorescent probes is crucial for understanding neural circuits and tumor dynamics.
    • Fluorescence diffusion tomography in reflection geometry (rFDT) enables deep tissue imaging but faces challenges with detection sensitivity and model accuracy.
    • Tissue heterogeneity and photon transport variations perturb diffusion paths, limiting current rFDT performance.

    Purpose of the Study:

    • To develop a novel method for precise 3D localization and sensing of intravital fluorescent probes at subcentimeter depths.
    • To enhance the accuracy and fidelity of fluorescence diffusion tomography in reflection geometry.
    • To overcome limitations of current rFDT methods related to detection sensitivity and photon transport perturbations.

    Main Methods:

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    • Introduced multierror-learning-enhanced rFDT (MEL-rFDT), a deep learning approach.
    • Embedded physical priors from photon transport models and spatial attention into the deep network.
    • Trained the network using limited in silico samples to adaptively compensate for errors and depth-dependent sensitivity.

    Main Results:

    • MEL-rFDT achieved high-fidelity reconstruction of intravital fluorescent probes.
    • Demonstrated unprecedented 3D localization and sensing accuracy in ex vivo brain tumor and in vivo subcutaneous tumor imaging in mice.
    • Showcased volumetric and functional generalization across samples.

    Conclusions:

    • MEL-rFDT significantly enhances the precision of 3D localization and sensing for intravital fluorescent probes.
    • The method's adaptive error compensation and sensitivity correction enable reliable deep tissue imaging.
    • Facilitates advancements in intraoperative pathology, dynamic imaging, and clinical decision-making.