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Updated: Jan 11, 2026

Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke
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A PINN-driven game-theoretic framework in limited data photoacoustic tomography.

Souvik Roy1, Suvra Pal1,2

  • 1Department of Mathematics, The University of Texas at Arlington, Arlington, TX 76019-0407, United States of America.

Inverse Problems
|November 17, 2025
PubMed
Summary

This study introduces a new method for better photoacoustic tomography reconstructions using game theory to fill in missing data. The approach enhances image contrast and resolution, even with limited information.

Keywords:
Nash equilibriumPontryagin’s maximum principlemachine learningsequential quadratic Hamiltoniantomographic imaging

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Inverse Problems

Background:

  • Limited data photoacoustic tomography (LD-PAT) poses reconstruction challenges.
  • Accurate image reconstruction is crucial for diagnostic applications.

Purpose of the Study:

  • To develop a novel framework for superior reconstructions in LD-PAT.
  • To address data incompleteness using game theory and advanced computational methods.

Main Methods:

  • Exploiting Cauchy data on accessible observation domains.
  • Employing a Nash game-theoretic framework to complete missing data.
  • Combining a gradient-free sequential quadratic Hamiltonian scheme with physics-informed neural networks.

Main Results:

  • Demonstrated effectiveness in numerical simulations with various phantoms and noise levels.
  • Achieved high contrast and resolution reconstructions.
  • Validated robustness across different accessible observation domains.

Conclusions:

  • The proposed framework offers a robust and accurate solution for LD-PAT.
  • This method significantly improves reconstruction quality in limited data scenarios.
  • The integration of game theory and neural networks shows promise for advanced imaging.