Evidence map›Paper›PMID 40335387›Full record

ArticleUltrasound in medicine & biology2025

Twin Peak Method for Estimating Tissue Viscoelasticity Using Shear Wave Elastography.

Shuvrodeb Adhikary, Matthew W Urban, Murthy N Guddati

Abstract read
In one paragraph

Article in Ultrasound in medicine & biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. High-Density Type I Collagen Promotes IFN-γAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Shuvrodeb AdhikaryNorth Carolina State University, Raleigh, NC, USA.
Matthew W UrbanMayo Clinic, Rochester, MN, USA.
Murthy N GuddatiNorth Carolina State University, Raleigh, NC, USA. Electronic address: mnguddat@ncsu.edu.

Funding

Measuring arterial material properties using wave-based approaches with ultrasound and computational models - Administrative SupplementR01HL145268 · NHLBI · MAYO CLINIC ROCHESTER · PI Matthew William Urban · 2019 to 2026
$5.9M
NHLBI NIH HHS R01 HL145268
6 · The paper itself

Abstract

Tissue viscoelasticity is becoming an increasingly useful biomarker beyond elasticity and can theoretically be estimated using shear wave elastography by inverting the propagation and attenuation characteristics of shear waves. Estimating viscosity is often more difficult than elasticity because attenuation, the main effect of viscosity, leads to poor signal-to-noise ratio of the shear wave motion. In the present work, we provide an alternative to existing methods of viscoelasticity estimation, based on peaks in the frequency-wavenumber (f-k) domain, which are considered more robust against noise compared with other features in the f-k domain. Specifically, the method minimizes the difference between simulated and measured versions of two sets of peaks (twin peaks) in the f-k domain, obtained first by traversing through each frequency and then by traversing through each wavenumber. The slopes and deviation of the twin peaks are sensitive to elasticity and viscosity, respectively, leading to the effectiveness of the proposed inversion algorithm for characterizing mechanical properties. This expected effectiveness is confirmed through in silico verification, followed by ex vivo validation and in vivo application, indicating that the proposed approach can be used effectively in accurately estimating viscoelasticity, thus potentially contributing to the development of enhanced biomarkers.

Indexed as

ElasticityElasticity Imaging TechniquesAlgorithmsComputer SimulationHumansPhantoms, ImagingViscosityAttenuationLiver elastographyPoint measurementsUltrasound elastographyViscosity

Identifiers

PMID40335387
PMCPMC12229767

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.