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Computed Tomography

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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: Jan 17, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

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.

Physics in Medicine and Biology

|September 16, 2025

Summary

This summary is machine-generated.

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 tomography image enhancement large foundation model supervised 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.