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 least-squares strain estimator for elastography

F Kallel1, J Ophir

  • 1University of Texas Medical School, Department of Radiology, Houston 77030, USA.

Ultrasonic Imaging
|July 1, 1997
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

Summaries of the papers of the 4th National Congress of the Tunisian Society of Medical Oncology attached to the 4th Maghreb Congress of Oncology.

La Tunisie medicale·2018
Same author

Elastography of beef muscle.

Meat science·2011
Same author

The use of elastography to measure quality characteristics of pork semimembranosus muscle.

Meat science·2011
Same author

The anti-inflammatory target A(3) adenosine receptor is over-expressed in rheumatoid arthritis, psoriasis and Crohn's disease.

Cellular immunology·2009
Same author

A deconvolution filter for improvement of time-delay estimation in elastography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2008
Same author

Multiresolution imaging in elastography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2008
Same journal

A Multi-Task Segmentation and Classification Network Based on Ultrasound Images for Predicting the Grading of Ascites in the Abdominal Cavity.

Ultrasonic imaging·2026
Same journal

Hybrid Physics-Driven Deep Learning for Enhanced Ultrasound Image Quality and Speckle Noise Suppression.

Ultrasonic imaging·2026
Same journal

Application of Super-Resolution Ultrasound Contrast Imaging in Differentiating Benign From Malignant Breast Tumors.

Ultrasonic imaging·2026
Same journal

A Novel Preprocessing Method for Common Carotid Artery Ultrasound Images Based on Phase Asymmetry Metric and Non-subsampled Shearlet Transform.

Ultrasonic imaging·2026
Same journal

Generalized Null Subtraction Factor: A Post-Filtering Framework for Contrast Enhancement in Ultrafast Ultrasound Imaging.

Ultrasonic imaging·2026
Same journal

Fourier Beamforming of Ultrasound Signals From Chirp Transmits Using the Chirp Scaling Algorithm.

Ultrasonic imaging·2026
See all related articles

A novel least-squares strain estimator (LSQSE) significantly enhances signal-to-noise ratio in elastography. This method improves detection sensitivity and dynamic range, though it may reduce strain contrast and spatial resolution.

Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Ultrasound Elastography

Background:

  • Elastography is a crucial medical imaging technique for assessing tissue mechanical properties.
  • Improving signal-to-noise ratio (SNRe) in elastograms is essential for accurate strain estimation.
  • Existing strain estimators face limitations in sensitivity and dynamic range.

Purpose of the Study:

  • To introduce and evaluate a least-squares strain estimator (LSQSE) for elastography.
  • To demonstrate the impact of LSQSE on signal-to-noise ratio (SNRe) and elastographic sensitivity.
  • To investigate the trade-offs associated with the LSQSE method.

Main Methods:

  • Development of a least-squares strain estimator (LSQSE).
  • Theoretical analysis using a modified strain filter.

Related Experiment Videos

  • Experimental validation with a homogeneous gel phantom.
  • Evaluation using simulated elastographic data.

    • Main Results:

    • The LSQSE significantly improved the signal-to-noise ratio (SNRe) in elastograms.
    • LSQSE demonstrated increased elastographic sensitivity, enabling detection of smaller strains.
    • A notable increase in the strain dynamic range was observed.
    • Simulations revealed a trade-off between improved SNRe and reduced strain contrast/spatial resolution.

    Conclusions:

    • The proposed LSQSE offers a substantial improvement in elastographic image quality and sensitivity.
    • LSQSE enhances the ability to detect subtle tissue variations.
    • Careful parameter selection is necessary to balance SNRe gains with potential reductions in contrast and resolution.