Evidence map›Paper›PMID 31647429›Full record

ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2020

An Improved Region-Growing Motion Tracking Method Using More Prior Information for 3-D Ultrasound Elastography.

Yuqi Wang, Matthew Bayer, Jingfeng Jiang, Timothy J Hall

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 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

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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. Article
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.

Yuqi Wang
Matthew Bayer
Jingfeng Jiang
Timothy J Hall

Funding

Quantitative Mechanical Imaging for Improving Breast Ultrasound DiagnosisR01CA140271 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI HALL, TIMOTHY J · 2009 to 2013
$3.0M
Elastography-based Analytics for Benign and Malignant Breast DiseaseR15EB026197 · NIBIB · MICHIGAN TECHNOLOGICAL UNIVERSITY · PI JIANG, JINGFENG · 2018 to 2018
$450k
NCI NIH HHS R01 CA140271NIBIB NIH HHS R15 EB026197
6 · The paper itself

Abstract

Three-dimensional (3-D) ultrasound elastography can provide 3-D tissue stiffness information that may be used during clinical diagnoses. In the framework of strain elastography, motion tracking plays an important role. In this study, an improved 3-D region-growing motion tracking (RGMT) algorithm based on a concept of exterior boundary points was developed. In principle, the proposed method first determines displacement at some seed points by strictly checking the local correlation and continuity in the neighborhood of those seeds. Subsequent displacement estimation is then conducted from these initial seeds to obtain displacements associated with other locations. This RGMT algorithm is designed to use more known information-including displacements and correlation values of all known-displacement neighboring points-to estimate the displacement of an unknown-displacement point, whereas previous RGMT methods employed information from only one such point. The algorithm was tested on 3-D ultrasound volumetric data acquired from a simulation, a tissue-mimicking phantom, and five human subjects. Motion-compensated cross correlations (MCCCs), strain contrast, and displacement Laplacian values (representing smoothness of an estimated displacement field) were calculated and used to evaluate the merits of the proposed RGMT method. Compared with a previously published RGMT method, the results show that the proposed RGMT method can provide smaller displacement errors and smoother displacements and improve strain contrast while maintaining reasonably high MCCC values, indicating good motion tracking quality. The proposed method is also computationally more efficient. In summary, our preliminary results demonstrated that the proposed RGMT algorithm is capable of obtaining high-quality 3-D strain elastographic data using modified clinical equipment.

Indexed as

AdultAlgorithmsBreastComputer SimulationElasticity Imaging TechniquesFemaleHumansImaging, Three-DimensionalMovementPhantoms, Imaging

Identifiers

PMID31647429
PMCPMC7159304

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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.