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Related Experiment Video

Updated: Jun 30, 2026

Author Spotlight: Optimizing Porous Substrate Electroporation Through Micro and Nanochannels for Enhanced Monitoring and Intermediate Stage Characterization

Author Spotlight: Optimizing Porous Substrate Electroporation Through Micro and Nanochannels for Enhanced Monitoring and Intermediate Stage Characterization

Published on: September 27, 2024

Enhanced Electroacoustic Tomography with Supervised Learning for Real-time Electroporation Monitoring.

Zhuoran Jiang¹, Yifei Xu², Leshan Sun²

¹Stanford University Stanford USA.

Precision Radiation Oncology

|May 8, 2025

Summary

This summary is machine-generated.

A new deep learning method enhances electroacoustic tomography (EAT) imaging for nanosecond pulsed electric fields (nsPEF) therapy. This improves real-time monitoring accuracy and clarity during treatment, overcoming previous image distortion limitations.

Keywords:

electroacoustic tomography electroporation interventional therapy limited‐angle reconstruction supervised learning

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Published on: September 6, 2024

Area of Science:

Medical Physics
Biomedical Engineering
Ultrasound Imaging

Background:

Nanosecond pulsed electric fields (nsPEF) offer synergistic potential with radiation therapy for improved treatment outcomes.
Electroacoustic tomography (EAT) monitors nsPEF energy deposition in real-time via ultrasound signals.
Current EAT utility is hindered by image distortions from limited ultrasound transducer views.

Purpose of the Study:

To develop a supervised learning workflow for correcting EAT image distortions.
To improve the accuracy and clarity of EAT imaging for nsPEF-based therapies.
To enable reliable intraoperative monitoring of electric energy deposition.

Main Methods:

Collected 56 experimental electroacoustic datasets from varying nsPEF intensities and geometries.
Utilized a custom rotating platform to acquire paired full-view and single-view signals.
Trained a deep learning model using supervised learning on 46 datasets for distortion correction.

Main Results:

Significantly improved image quality in linear array-based EAT.
Generated pressure maps with accurate and clear structural representations.
Achieved low-intensity error (RMSE: 0.018), high PSNR (35.15), and high SSIM (0.942) for enhanced single-view images.

Conclusions:

Pioneering achievement of high-quality EAT using a single linear array in an experimental setting.
Demonstrated the effectiveness of a deep learning approach for EAT image reconstruction.
Enhanced EAT's practical utility for real-time monitoring in nsPEF electroporation therapy.