Evidence map›Paper›PMID 33151880›Full record

ArticleIEEE transactions on medical imaging2021

Repeatability of Linear and Nonlinear Elastic Modulus Maps From Repeat Scans in the Breast.

Daniel I Gendin, Rohit Nayak, Yuqi Wang, Mahdi Bayat, Robert T Fazzio, Assad A Oberai, Timothy J Hall, Paul E Barbone, Azra Alizad, Mostafa Fatemi

Abstract read
In one paragraph

Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Lagrangian deformation tracking for strain imaging.Progress in biomedical engineering (Bristol, England) · 2026
    Review
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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

10 authors.

Daniel I Gendin
Rohit Nayak
Yuqi Wang
Mahdi Bayat
Robert T Fazzio
Assad A Oberai
Timothy J Hall
Paul E Barbone
Azra Alizad
Mostafa Fatemi

Funding

Multi-parameter Nonlinear Elasticity Mapping of Breast MassesR01CA195527 · NCI · MAYO CLINIC ROCHESTER · PI ALIZAD, AZRA, FATEMI, MOSTAFA · 2016 to 2020
$2.8M
NCI NIH HHS R01 CA195527
6 · The paper itself

Abstract

Compression elastography allows the precise measurement of large deformations of soft tissue in vivo. From an image sequence showing tissue undergoing large deformation, an inverse problem for both the linear and nonlinear elastic moduli distributions can be solved. As part of a larger clinical study to evaluate nonlinear elastic modulus maps (NEMs) in breast cancer, we evaluate the repeatability of linear and nonlinear modulus maps from repeat measurements. Within the cohort of subjects scanned to date, 20 had repeat scans. These repeated scans were processed to evaluate NEM repeatability. In vivo data were acquired by a custom-built, digitally controlled, uniaxial compression device with force feedback from the pressure-plate. RF-data were acquired using plane-wave imaging, at a frame-rate of 200 Hz, with a ramp-and-hold compressive force of 8N, applied at 8N/sec. A 2D block-matching algorithm was used to obtain sample-level displacement fields which were then tracked at subsample resolution using 2D cross correlation. Linear and nonlinear elasticity parameters in a modified Veronda-Westmann model of tissue elasticity were estimated using an iterative optimization method. For the repeated scans, B-mode images, strain images, and linear and nonlinear elastic modulus maps are measured and compared. Results indicate that when images are acquired in the same region of tissue and sufficiently high strain is used to recover nonlinearity parameters, then the reconstructed modulus maps are consistent.

Indexed as

BreastElasticity Imaging TechniquesAlgorithmsElasticityElastic ModulusHumansPhantoms, Imaging

Identifiers

PMID33151880
PMCPMC11017922

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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.