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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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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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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Quantification of thyroid nodules in multiple ultrasonography systems.

Young-Min Kim1, Myeong-Gee Kim2, Seok-Hwan Oh2

  • 1School of Electrical Engineering Korea Advanced Institute of Science and Technology, Daejeon, South Korea.

Medical Image Analysis
|March 18, 2026
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Summary

This study introduces QIT-net, a quantitative imaging technique using a hybrid CNN-Transformer model to assess thyroid nodules by measuring acoustic properties. The method ensures reliable performance across various ultrasound systems, enhancing diagnostic accuracy.

Keywords:
Deep neural networkMedical ultrasoundQuantitative imagingQuantitative ultrasound imagingTransformer

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Quantitative ultrasound (QUS) extracts tissue acoustic properties from pulse-echo signals.
  • Assessing thyroid nodules requires accurate quantification of acoustic properties like attenuation (ATT) and speed of sound.
  • Variability across ultrasound systems poses challenges for consistent quantitative analysis.

Purpose of the Study:

  • To introduce QIT-net, a quantitative imaging technique for thyroid nodule assessment.
  • To quantify acoustic attenuation (ATT) and speed of sound using multiple ultrasonography systems.
  • To develop a robust method that handles variations in ultrasound device characteristics.

Main Methods:

  • Employed a CNN-Transformer hybrid architecture to analyze radiofrequency (RF) data, capturing both local and global features.
  • Utilized B-mode images as auxiliary input for improved performance in complex anatomical structures.
  • Implemented a device-specific fine-tuning strategy and a parameter-sharing network architecture for cross-system training.

Main Results:

  • The QIT-net demonstrated effective quantification of acoustic attenuation and speed of sound for thyroid nodules.
  • The hybrid architecture successfully integrated local and global feature extraction from RF data.
  • Auxiliary B-mode imaging and fine-tuning strategies enhanced robustness across different ultrasound devices.

Conclusions:

  • QIT-net provides a reliable quantitative imaging technique for thyroid nodule assessment across diverse ultrasonography systems.
  • The proposed deep learning approach effectively quantifies key acoustic properties.
  • This method holds potential for improving diagnostic accuracy in thyroid nodule evaluation.