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Related Experiment Video

Updated: Jun 2, 2026

Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging
06:52

Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging

Published on: January 26, 2024

Sparsity-driven reconstruction for FDOT with anatomical priors.

Jean-Charles Baritaux1, Kai Hassler, Martina Bucher

  • 1Swiss Federal Institute of Technology of Lausanne, 1015 Lausanne, Switzerland. jean-charles.baritaux@epfl.ch

IEEE Transactions on Medical Imaging
|April 22, 2011
PubMed
Summary

We introduce a new method using (2, 1)-mixed-norm penalization for fluorescence-guided optical tomography (FDOT) image reconstruction. This technique enhances structural prior information, improving accuracy in identifying fluorescent probe accumulation in anatomical regions.

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Computational Imaging

Background:

  • Fluorescence-guided optical tomography (FDOT) is crucial for visualizing fluorescent probe accumulation.
  • Accurate image reconstruction requires incorporating structural information and effective regularization.
  • Existing methods may not fully leverage structural priors for enhanced resolution and specificity.

Purpose of the Study:

  • To propose a novel image reconstruction method for FDOT using (2, 1)-mixed-norm penalization.
  • To incorporate structural priors into the FDOT reconstruction process.
  • To develop an efficient numerical method for solving the associated optimization problem.

Main Methods:

  • A variational framework is employed for FDOT image reconstruction.

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

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Last Updated: Jun 2, 2026

Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging
06:52

Optical Clearing and Labeling for Light-sheet Fluorescence Microscopy in Large-scale Human Brain Imaging

Published on: January 26, 2024

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

  • The core of the method is (2, 1)-mixed-norm penalization, which provides both sparsifying and regularization effects.
  • A practical numerical algorithm is derived to solve the optimization problem, accommodating various sparsity-promoting regularization techniques like L1-norm and total variation.

    • Main Results:

    • The (2, 1)-mixed-norm penalization effectively isolates regions of fluorescent probe accumulation.
    • The method provides regularization within the selected anatomical regions, enhancing image quality.
    • The proposed approach demonstrates successful application on both synthetic and experimental FDOT data.

    Conclusions:

    • The proposed (2, 1)-mixed-norm penalization method offers an effective way to incorporate structural priors in FDOT image reconstruction.
    • This approach enhances the identification of fluorescent probe accumulation and improves image regularization.
    • The method is versatile, encompassing other sparsity-promoting techniques and showing promise for real-world applications.