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  1. Home
  3. Enhancing Fluorescence Lifetime Imaging With Differential Transformer.
  1. Home
  3. Enhancing Fluorescence Lifetime Imaging With Differential Transformer.

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Fluorescence Lifetime Imaging of Molecular Rotors in Living Cells
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Enhancing Fluorescence Lifetime Imaging With Differential Transformer.

Ismail Erbas1,2, Vikas Pandey1,2, Navid Ibtehaj Nizam3

  • 1Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA.

Journal of Biophotonics
|February 1, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

MFliNet corrects topographical distortions in fluorescence lifetime imaging (FLI) using a novel deep learning approach. This enables accurate, real-time macroscopic FLI for complex biological and intraoperative applications.

Keywords:
MFLIcancer imagingdeep learningdifferential transformerfluorescence lifetime imaging

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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Deep Learning

Background:

  • Conventional fluorescence lifetime imaging (FLI) relies on accurate instrument response function (IRF) and decay data.
  • IRF variations at macroscopic scales hinder precise lifetime parameter estimation.
  • Current deep learning methods struggle with complex biological and in vivo imaging data.

Purpose of the Study:

  • To develop a robust deep learning framework, MFliNet, for accurate multi-exponential decay parameter estimation in FLI.
  • To address topographical distortions affecting IRF and photon arrival distributions.
  • To enable reliable, real-time macroscopic FLI for challenging applications.

Main Methods:

  • Introduced MFliNet, a deep learning framework utilizing a Differential Transformer encoder-decoder architecture.
  • MFliNet jointly processes temporal fluorescence decay and IRF inputs.
  • The model incorporates photon time-of-flight deconvolution principles.

    • Main Results:

    • MFliNet effectively corrects topographical distortions in photon arrival distributions.
    • Validated with tissue-mimicking phantoms and preclinical tumor models.
    • Demonstrated exceptional robustness and precision in macroscopic FLI.

    Conclusions:

    • MFliNet provides accurate multi-exponential decay parameter estimation.
    • The framework enables reliable, real-time macroscopic FLI.
    • MFliNet is suitable for complex biological and intraoperative imaging scenarios.