Evidence map›Paper›PMID 31562083›Full record

ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2020

Measurement of Viscoelastic Material Model Parameters Using Fractional Derivative Group Shear Wave Speeds in Simulation and Phantom Data.

Courtney A Trutna, Ned C Rouze, Mark L Palmeri, Kathryn R Nightingale

Abstract read
In one paragraph

Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Courtney A Trutna
Ned C Rouze
Mark L Palmeri
Kathryn R Nightingale

Funding

Training in Medical ImagingT32EB001040 · NIBIB · DUKE UNIVERSITY · PI NIGHTINGALE, KATHRYN RADABAUGH · 2003 to 2018
$4.4M
Improved ultrasound imaging using elevated acoustic outputR01EB022106 · NIBIB · DUKE UNIVERSITY · PI NIGHTINGALE, KATHRYN RADABAUGH · 2016 to 2019
$1.8M
NIBIB NIH HHS R01 EB022106NIBIB NIH HHS T32 EB001040
6 · The paper itself

Abstract

While ultrasound shear wave elastography originally focused on tissue stiffness under the assumption of elasticity, recent work has investigated the higher order, viscoelastic properties of the tissue. This article presents a method to use group shear wave speeds (gSWSs) at a series of derivative orders to characterize viscoelastic materials. This method uses a least squares fitting algorithm to match the experimental data to the calculated gSWS data, using an assumed material model and excitation geometry matched to the experimental imaging configuration. Building on a previous study that used particle displacement, velocity, and acceleration signals, this study extends the analysis to a continuous range of fractional derivative orders between 0 and 2. The method can be applied to any material model. Herein, material characterization was performed for three different two-parameter models and three different three-parameter models. This group speed-based method was applied to both shear wave simulations with ultrasonic tracking and experimental acquisitions in viscoelastic phantoms [similar to the Phase II Quantitative Imaging Biomarkers Alliance (QIBA) phantoms]. In both the cases, the group speed method produced more repeatable characterization overall than fitting the phase velocity results from the peak of the 2-D Fourier transform. Results suggest that the linear attenuation model is a better fit than the Voigt model for the viscoelastic QIBA phantoms.

Indexed as

Phantoms, ImagingComputer SimulationElasticityElasticity Imaging TechniquesLeast-Squares AnalysisSignal Processing, Computer-AssistedViscosity

Identifiers

PMID31562083
PMCPMC7029806

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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.