Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A simulation algorithm for ultrasound liver backscattered signals

D Zatari1, N Botros, F Dunn

  • 1Department of Electrical Engineering, Southern Illinois University, Carbondale 62901, USA.

Ultrasonics
|November 1, 1995
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Student teachers as tutors for children in an inner city school.

Child psychiatry and human development·2013
Same author

Secondhand smoke exposure and survival following acute coronary syndrome: prospective cohort study of 1261 consecutive admissions among never-smokers.

Heart (British Cardiac Society)·2009
Same author

Balloon angioplasty for native aortic coarctation in different anatomic variants.

Pediatric cardiology·2007
Same author

Are chest pain observation units essential for rapid and effective emergency care in the UK?

Emergency medicine journal : EMJ·2006
Same author

Plastic baton round injuries.

Emergency medicine journal : EMJ·2005
Same author

Team triage improves emergency department efficiency.

Emergency medicine journal : EMJ·2004
Same journal

Grain size evaluation in polycrystalline materials using the interference effect of bounded ultrasonic beams at the Rayleigh critical angle.

Ultrasonics·2026
Same journal

Ultrasonic characterization of functionally graded materials using a continuously graded model and spectral inversion.

Ultrasonics·2026
Same journal

Frequency-wavenumber domain inversion for arterial viscoelasticity.

Ultrasonics·2026
Same journal

Pressure- and frequency-dependent acoustic behavior of second-generation acoustic reporter genes-expressing bacteria for optimized ultrasound imaging.

Ultrasonics·2026
Same journal

Laser ultrasonic detection for strut defects in additively manufactured lattice structure using zero-group-velocity Lamb waves.

Ultrasonics·2026
Same journal

A hemispherical bubble induced by ultrasonic vibration observed by high-speed X-ray imaging.

Ultrasonics·2026
See all related articles

This study introduces a simulation algorithm for liver ultrasound signals, accurately modeling normal and abnormal liver tissue backscatter. The algorithm demonstrates satisfactory performance in classifying liver abnormalities using artificial neural networks.

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Acoustics

Background:

  • Ultrasound imaging is crucial for liver disease diagnosis.
  • Accurate simulation of ultrasound signals can aid in developing diagnostic tools.
  • Distinguishing between normal and abnormal liver tissue using ultrasound is challenging.

Purpose of the Study:

  • To develop and validate a simulation algorithm for backscattered ultrasound signals from liver tissue.
  • To simulate signals from normal liver and three types of liver abnormalities.
  • To assess the algorithm's performance in classifying liver abnormalities.

Main Methods:

  • Developed a simulation algorithm for backscattered ultrasound signals.
  • Simulated signals for normal liver and three abnormal conditions.

Related Experiment Videos

  • Statistically compared simulated signals with in vivo data.
  • Applied an artificial neural network for signal classification.
  • Extracted acoustic features: attenuation coefficient and speed of sound dispersion.

    • Main Results:

    • The simulation algorithm performed satisfactorily.
    • Statistical comparison validated the simulated signals against in vivo data.
    • Artificial neural network successfully classified simulated signals based on abnormalities.
    • A custom data acquisition and analysis system was used for further testing.

    Conclusions:

    • The developed simulation algorithm is a reliable tool for modeling liver ultrasound signals.
    • The algorithm can accurately represent backscattered signals from various liver conditions.
    • This simulation approach supports the development of advanced ultrasound-based diagnostic methods for liver diseases.