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Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

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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...
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Ultrasonography01:17

Ultrasonography

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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.
During an ultrasonography procedure, a handheld device called...
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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Ultrasound II: Endoscopic Ultrasound and FibroScan01:25

Ultrasound II: Endoscopic Ultrasound and FibroScan

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Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
Endoscopic Ultrasound (EUS):
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Related Experiment Video

Updated: Jan 15, 2026

Formulation and Acoustic Modulation of Optically Vaporized Perfluorocarbon Nanodroplets
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Coating Thickness Estimation Using a CNN-Enhanced Ultrasound Echo-Based Deconvolution.

Marina Perez-Diego1,2, Upeksha Chathurani Thibbotuwa1, Ainhoa Cortés1,2

  • 1CEIT-Basque Research and Technology Alliance (BRTA), Manuel Lardizabal 15, 20018 Donostia-San Sebastián, Spain.

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|October 16, 2025
PubMed
Summary

This study introduces a new ultrasound method to accurately measure coating thickness, even with overlapping echoes. The technique uses signal analysis and a 1D convolutional neural network for reliable non-destructive testing in offshore applications.

Keywords:
CNNcoating thickness estimationdeconvolution modellingultrasound pulse-echo

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Area of Science:

  • Materials Science
  • Acoustics
  • Non-Destructive Testing

Background:

  • Coating degradation monitoring is vital for offshore structural integrity and corrosion prevention.
  • Accurate coating thickness estimation is crucial for assessing protective layer performance.
  • Traditional ultrasound pulse-echo methods struggle with overlapping echoes from closely spaced interfaces, hindering precise thickness measurement.

Purpose of the Study:

  • To develop an advanced ultrasound-based methodology for accurate coating thickness estimation.
  • To overcome limitations of conventional methods in resolving closely spaced acoustic interfaces.
  • To enable reliable non-destructive monitoring of coating integrity in demanding environments.

Main Methods:

  • A novel deconvolution model was developed using two consecutive backwall echoes to isolate propagation path information.
  • A 1D convolutional neural network (1D-CNN) was employed for enhanced detection of coating thickness within the reflectivity function.
  • Synthetic signals were generated using finite-difference time-domain (FDTD) simulations (k-Wave MATLAB toolbox) for training the 1D-CNN model.

Main Results:

  • The methodology successfully estimated front-side coating thickness (60μm–740μm) on steel substrates under varied conditions.
  • The minimum detectable thickness was approximately λ/5 for an 8 MHz ultrasonic transducer.
  • The 1D-CNN model achieved an accuracy of approximately 8μm on synthetic data.

Conclusions:

  • The proposed ultrasound echo-based approach offers a robust solution for coating thickness estimation.
  • The method demonstrates significant potential for reliable monitoring of coating thickness changes in real-world applications.
  • This technique enhances non-destructive testing capabilities for offshore industries and beyond.