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Related Experiment Video

Updated: Apr 12, 2026

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
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Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization.

Junwei Shi1, Fei Liu2, Jiulou Zhang1

  • 1Tsinghua University, Department of Biomedical Engineering, School of Medicine, Haidian District, Beijing 100084, China.

Journal of Biomedical Optics
|May 14, 2015
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Summary

A new discrete cosine transform (DCT) based reweighted L1-norm regularization algorithm improves fluorescence molecular tomography (FMT) reconstruction. This method enhances signal-to-noise ratio (SNR) in preclinical imaging, even with limited projections.

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Area of Science:

  • Biomedical Imaging
  • Medical Physics
  • Computational Biology

Background:

  • Fluorescence molecular tomography (FMT) is a key noninvasive imaging technique for preclinical research.
  • FMT reconstruction is often ill-posed, particularly with limited projection data, compromising image quality.
  • Existing regularization methods struggle to effectively suppress noise and accelerate convergence in FMT.

Purpose of the Study:

  • To develop and evaluate a novel discrete cosine transform (DCT) based reweighted L1-norm regularization algorithm for enhanced FMT reconstruction.
  • To improve the signal-to-noise ratio (SNR) and convergence speed of FMT imaging, especially under limited-projection conditions.
  • To address the inherent ill-posedness of FMT reconstruction using adaptive regularization strategies.

Main Methods:

  • A discrete cosine transform (DCT) based reweighted L1-norm regularization algorithm was proposed for FMT reconstruction.
  • Adaptive reweighting of regularization parameters based on DCT coefficients was implemented to reduce noise.
  • An adaptive permission region was constructed to accelerate the convergence of the reconstruction process.
  • The algorithm was validated using physical phantom and in vivo mouse experiments with a limited number of projections (four).

Main Results:

  • The proposed DCT-based reweighted L1-norm regularization algorithm demonstrated superior performance compared to other L1-norm strategies.
  • Quantitative analysis showed a significantly higher signal-to-noise ratio (SNR) in reconstruction results from both phantom and in vivo mouse experiments.
  • The adaptive construction of the permission region contributed to increased convergence speed during reconstruction.

Conclusions:

  • The DCT-based reweighted L1-norm regularization is an effective method for improving FMT reconstruction quality, particularly with limited data.
  • This novel approach offers enhanced SNR and faster convergence, making it valuable for preclinical biomedical applications.
  • The adaptive regularization strategy provides a robust solution to the ill-posed nature of FMT imaging.