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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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Related Experiment Video

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An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging
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Quantitative Ultrasound: Experimental Implementation.

Michael Oelze1

  • 1Department of Electrical and Computer Engineering and Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, USA. oelze@illinois.edu.

Advances in Experimental Medicine and Biology
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Summary
This summary is machine-generated.

Estimating the backscatter coefficient, a key tissue property, requires system independence. This study explores methods for calibration and noise reduction to achieve accurate, operator-independent tissue characterization.

Keywords:
Attenuation compensationCalibrationPlanar referenceReference phantomWindow functions

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Area of Science:

  • Biomedical Ultrasound
  • Acoustic Imaging
  • Tissue Characterization

Background:

  • The backscatter coefficient is a fundamental tissue property, analogous to attenuation and sound speed.
  • It provides insights into scatterer properties and underlying tissue states.
  • Accurate estimation is crucial for reliable tissue characterization.

Purpose of the Study:

  • To achieve system- and operator-independent estimates of the backscatter coefficient.
  • To discuss methods for obtaining a calibration spectrum.
  • To address deterministic noise reduction in backscatter coefficient spectrum estimation.

Main Methods:

  • Discusses three distinct approaches for acquiring a calibration spectrum.
  • Compares the engineering trade-offs inherent in each calibration method.
  • Considers techniques for mitigating deterministic noise in the backscatter coefficient spectrum.

Main Results:

  • Identifies key considerations for system- and operator-independent backscatter coefficient estimation.
  • Provides a comparative analysis of different calibration spectrum acquisition strategies.
  • Outlines methods for deterministic noise reduction applicable to backscatter coefficient spectra.

Conclusions:

  • System- and operator-independent estimation of the backscatter coefficient is achievable through proper calibration.
  • Understanding engineering trade-offs is vital for selecting the optimal calibration approach.
  • Effective noise reduction techniques are essential for accurate backscatter coefficient spectrum analysis.