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3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
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Three-dimensional quantitative elastography using micro-ultrasound: Proof of concept.

Reid Vassallo1, Tajwar Abrar Aleef1, Qi Zeng2

  • 1School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, V6T 2B9, BC, Canada.

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|October 3, 2025
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Summary

This study introduces volumetric shear wave absolute vibro-elastography (S-WAVE) using micro-ultrasound (microUS) for prostate cancer (PCa) detection. The method successfully visualized stiffness variations in a phantom, showing promise for improved PCa diagnostics.

Keywords:
Micro-ultrasound (microUS)Prostate cancerShear wave elastography (SWE)Vibro-elastography (VE)

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Oncology

Background:

  • Prostate cancer (PCa) diagnosis faces limitations in current workflows.
  • Quantitative elastography and micro-ultrasound (microUS) offer potential solutions.
  • There is a need for advanced imaging techniques for accurate PCa detection.

Purpose of the Study:

  • To present the first implementation of volumetric shear wave absolute vibro-elastography (S-WAVE) using the ExactVu™ microUS system.
  • To validate the feasibility of microUS-based S-WAVE for clinical integration.
  • To assess the accuracy of S-WAVE in measuring tissue stiffness variations.

Main Methods:

  • Implementation of volumetric S-WAVE using ExactVu™ microUS with minimal hardware.
  • Employment of a bandpass sampling strategy for tissue motion tracing below Nyquist rate.
  • Validation using a commercial quality assurance phantom with inclusions of varying stiffness.

Main Results:

  • Successful visualization of all four inclusions with expected relative stiffness values.
  • Accurate and repeatable measurement of background stiffness.
  • Demonstration of microUS-based S-WAVE's effectiveness in detecting stiffness variations.

Conclusions:

  • MicroUS-based S-WAVE imaging is effective for visualizing tissue stiffness.
  • This technique shows significant potential for the future detection of PCa lesions.
  • The implementation is conducive to clinical workflow integration due to minimal hardware requirements.