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3D electroacoustic tomography image enhancement using deep learning with the SAM-Med3D encoder.

Yankun Lang1, Jadon Buller2, Yifei Xu2

  • 1Department of Radiation Oncology Physics, University of Maryland, Baltimore, Baltimore, MD 21201, United States of America.

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|September 16, 2025
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Summary

This study introduces a deep learning framework using SAM-Med3D to improve 3D electroacoustic tomography (EAT) imaging from limited-angle data, enabling faster and more accurate visualization for electroporation therapies.

Keywords:
electroacoustic tomographyimage enhancementlarge foundation modelsupervised deep learning

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Electroacoustic tomography (EAT) faces limitations in clinical settings due to artifacts from limited-angle data acquisition.
  • Accurate visualization of electric fields is crucial for electroporation-based therapies.

Purpose of the Study:

  • To develop a deep learning framework for enhancing 3D EAT image reconstruction from single-view projections.
  • To overcome artifacts and distortions in EAT imaging for improved clinical application.

Main Methods:

  • A novel deep learning framework leveraging the SAM-Med3D large foundation model (LFM) was developed.
  • The framework features a modified encoder for local-global feature fusion and a lightweight decoder for high-resolution image generation.
  • The model was trained and validated on a dataset of 50 EAT scans (6000 views).

Main Results:

  • The proposed model significantly outperformed baseline 3D U-Nets with superior RMSE, PSNR, and SSIM metrics.
  • Achieved reconstruction of a full-view 3D EAT image from a single view in 2 seconds.
  • Demonstrated potential for near real-time monitoring and adaptive dose verification in electroporation therapies.

Conclusions:

  • This work presents the first application of SAM-Med3D for enhancing 3D EAT imaging.
  • The framework effectively addresses the challenge of limited-angle data in EAT.
  • The approach holds significant potential for enhancing precision and safety in electroporation-based therapies, increasing clinical viability.