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Sensing Through Tissues Using Diffuse Optical Imaging and Genetic Programming.

Ganesh M Balasubramaniam1, Ami Hauptman1, Shlomi Arnon1

  • 1Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Be'er Sheva 8441405, Israel.

Sensors (Basel, Switzerland)
|January 10, 2026
PubMed
Summary
This summary is machine-generated.

We developed Diffuse Optical Imaging using Genetic Programming (DI-GP), a novel AI framework for accurate, fast, and interpretable medical imaging. DI-GP overcomes limitations in diffuse optical imaging, enabling deeper tissue visualization for clinical applications.

Keywords:
diffuse mediadiffuse optical imaginggenetic programmingimage reconstructioninverse problemsmachine learningsensing

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Area of Science:

  • Biomedical optics
  • Medical imaging
  • Artificial intelligence

Background:

  • Diffuse optical imaging (DOI) faces challenges in clinical adoption due to the complex inverse problem of light scattering in biological tissues.
  • Existing reconstruction algorithms struggle with nonlinearity and ill-posedness, limiting DOI's application in areas like breast and brain imaging.
  • Limited datasets and physical constraints further hinder the widespread use of DOI.

Purpose of the Study:

  • To introduce Diffuse Optical Imaging using Genetic Programming (DI-GP), a physics-guided and interpretable AI framework for DOI.
  • To develop a method that addresses the nonlinear, ill-posed inverse problem in diffuse media reconstruction.
  • To enhance the speed, accuracy, and interpretability of DOI reconstructions.

Main Methods:

  • Developed DI-GP, a genetic programming framework grounded in the diffusion equation.
  • Evolved closed-form symbolic mappings for 2-D reconstructions in scattering media.
  • Validated the approach using simulations and tabletop experiments in tissue-like media.

Main Results:

  • DI-GP achieved substantially faster inference and improved reconstruction performance compared to analytical methods.
  • Successfully recovered targets at depths exceeding 25 transport mean-free paths without prior knowledge of shape or location.
  • Demonstrated centimeter-scale imaging in tissue-like media, showcasing potential for deep-tissue visualization.

Conclusions:

  • DI-GP offers a physics-guided, interpretable, and efficient solution for diffuse optical imaging.
  • The framework's transparency and data efficiency make it suitable for regulated domains requiring explainable AI.
  • DI-GP shows significant promise for advancing non-invasive deep-tissue imaging and practical DOI systems.