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Imaging Studies II: Ultrasonography

Imaging Studies II: Ultrasonography

IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...

Ultrasonography

Ultrasonography

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.
During an ultrasonography procedure, a handheld device called...

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Updated: Apr 2, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Time-reversal-based ultrasonic non-destructive evaluation using convolutional neural networks.

Jiin Seo¹, Ji-Yun Kim¹, Je-Heon Han²

¹Department of Mechanical Engineering, Tech University of Korea, 237 Sangidaehak-ro, Siheung-si, Gyeonggi-do 15073, the Republic of Korea.

|March 31, 2026

Summary

This study introduces a novel non-destructive evaluation (NDE) method using a convolutional neural network (CNN) trained with time-reversal (TR) signals for defect detection in composite materials. The approach significantly improves accuracy in identifying structural defects in aerospace components.

Keywords:

CNN Non-destructive testing Time-reversal (TR) method

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Area of Science:

Materials Science
Mechanical Engineering
Signal Processing

Background:

Ultrasonic non-destructive evaluation (NDE) faces challenges in detecting defects in composite materials and complex structures due to signal scattering.
Accurate defect identification is crucial for aerospace and space launch vehicle safety and reliability.

Purpose of the Study:

To develop a highly accurate NDE method for composite materials and complex structures.
To leverage convolutional neural networks (CNNs) and time-reversal (TR) signals for enhanced defect detection.
To validate the proposed method on honeycomb sandwich panels and skin-stringer structural models.

Main Methods:

A finite element model was validated against experimental data from an aluminum panel.
A CNN-based classification framework was trained using time-reversal (TR) signals.
The method was applied to honeycomb sandwich panels and skin-stringer structural models for defect classification.

Main Results:

The CNN model achieved high classification accuracy when trained with TR signals.
The finite element model demonstrated the algorithm's effectiveness on complex structural models.
The time-reversal method successfully generated strong signals at defect locations, improving detection.

Conclusions:

The proposed CNN-based NDE method using TR signals offers a robust and high-performance solution for defect detection.
The study confirms the effectiveness of acquiring large, reflected signals from defects through time reversal for accurate classification.
This technique is particularly promising for NDE applications in aerospace and complex mechanical structures.