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Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Related Experiment Video

Updated: Jul 2, 2026

Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke
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MUnet: A model-based unrolled reconstruction framework for computational photoacoustic imaging.

Qi Liu1, Le Li1, Zifeng Zhao1

  • 1School of Physics, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China.

Ultrasonics
|June 30, 2026
PubMed
Summary

We developed MUnet, a novel deep learning method for photoacoustic imaging (PAI). MUnet enhances image quality and reduces computation time, making real-time clinical PAI more feasible.

Keywords:
Data-fidelity termNeural networkPhotoacoustic imagingUnrolled framework

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Area of Science:

  • Medical imaging
  • Biomedical engineering
  • Computational imaging

Background:

  • Photoacoustic imaging (PAI) offers unique optical contrast and ultrasonic penetration for in vivo biological visualization.
  • Robust PAI image reconstruction is hindered by limited-view detection, sparse sampling, and noise.
  • Current reconstruction methods involve trade-offs between efficiency, accuracy, and interpretability.

Purpose of the Study:

  • To develop an advanced image reconstruction method for photoacoustic imaging.
  • To overcome the limitations of existing PAI reconstruction techniques, particularly under challenging conditions.
  • To improve the speed and fidelity of PAI for potential real-time clinical applications.

Main Methods:

  • Proposed MUnet, a model-based unrolled deep learning architecture.
  • Integrated a physics-informed data-fidelity term with a supervised image-domain enhancement module.
  • Alternated non-trainable analytic updates with neural image refinement for enhanced reconstruction.

Main Results:

  • MUnet demonstrated superior performance compared to traditional and learning-based methods on in vivo PAI data.
  • Achieved significantly improved image quality and reduced computation time.
  • Validated the effectiveness of combining model-based interpretability with deep learning flexibility.

Conclusions:

  • MUnet offers a promising solution for rapid and high-fidelity photoacoustic imaging.
  • The proposed method enhances PAI reconstruction robustness and efficiency.
  • MUnet advances the potential of PAI for real-time clinical diagnostics and imaging.