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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
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Related Experiment Video

Updated: Jul 28, 2025

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Ultrasonic imaging using conditional generative adversarial networks.

Nathan Molinier¹, Guillaume Painchaud-April², Alain Le Duff²

¹PULÉTS, École de Technologie Supérieure (ÉTS), Montréal, H3C 1K3, QC, Canada.

|June 3, 2023

Summary

This summary is machine-generated.

This study introduces a faster ultrasonic nondestructive testing method using conditional Generative Adversarial Networks (cGANs) to create Total Focusing Method (TFM) images from plane wave (PW) data, significantly reducing processing time and file size.

Keywords:

Deep learning Generative models TFM Ultrasonic imaging Ultrasonic phased array cGAN

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Area of Science:

Materials Science
Engineering
Computer Science

Background:

Full Matrix Capture (FMC) and Total Focusing Method (TFM) are standard for ultrasonic nondestructive testing.
FMC data acquisition and TFM processing are time-consuming, limiting high-cadence inspections.

Purpose of the Study:

To develop a faster alternative to conventional FMC-TFM for ultrasonic nondestructive testing.
To evaluate the performance of conditional Generative Adversarial Networks (cGANs) in reconstructing TFM-like images from plane wave (PW) data.

Main Methods:

Proposed replacing FMC acquisition with single zero-degree plane wave (PW) insonification.
Employed conditional Generative Adversarial Networks (cGANs) trained to generate TFM-like images.
Compared three cGAN models with conventional TFM computed from FMC data.

Main Results:

cGANs recreated TFM-like images with comparable resolution to conventional TFM.
Image contrast was improved in over 94% of reconstructions.
Reduced background noise and eliminated artifacts through biased cGAN training.
Achieved a 120x reduction in computation time and a 75x reduction in file size.

Conclusions:

The proposed cGAN-based method offers a significantly faster and more efficient approach to ultrasonic nondestructive testing.
This technique maintains image resolution while enhancing contrast and reducing artifacts.
The method is suitable for high-cadence inspections where traditional FMC-TFM is impractical.