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Related Experiment Video

Updated: May 12, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
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Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

LoGoReg-Net: local-global feature aggregation regularized unrolling network for functional brain imaging via

Di Wu1, Huiting Qiao1, Deyu Li1

  • 1Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.

Biomedical Optics Express
|May 11, 2026
PubMed
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This study introduces LoGoReg-Net, an advanced unrolling network for high-density diffuse optical tomography (HD-DOT). It improves brain imaging accuracy and efficiency by effectively modeling optical perturbations for better clinical translation.

Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Computational Science

Background:

  • Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging tool used in clinical and rehabilitation settings.
  • High-density diffuse optical tomography (HD-DOT) improves fNIRS spatial resolution but faces challenges in accuracy and computational efficiency due to its ill-posed nature.
  • Existing unrolling network methods struggle to model local and global optical perturbations effectively and can be computationally expensive.

Purpose of the Study:

  • To develop an efficient and accurate HD-DOT reconstruction method.
  • To address the limitations of existing unrolling networks in modeling optical perturbations.
  • To enhance the clinical applicability of HD-DOT imaging.

Main Methods:

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Last Updated: May 12, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
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  • Proposed LoGoReg-Net, an unrolling network incorporating a local-global feature aggregation module (LGFAM).
  • LGFAM features a multi-scale local feature capture module and a triple-coordinate global feature aggregation module.
  • Validated through numerical simulations and physical phantom experiments.

    • Main Results:

    • LoGoReg-Net demonstrated superior performance over existing methods on various datasets (in-distribution, out-of-distribution, real-world).
    • Achieved higher SSIM and PSNR values, indicating improved structural fidelity and fine-detail recovery.
    • Performance gains were maintained across different wavelengths, showing robustness.

    Conclusions:

    • LoGoReg-Net effectively overcomes current bottlenecks in HD-DOT imaging.
    • The method offers a promising solution for advancing high-performance brain functional imaging technologies.
    • The approach holds significant potential for clinical translation of HD-DOT.