Evidence map›Paper›PMID 28586393›Full record

ArticlePloS one2017

A numerical framework for interstitial fluid pressure imaging in poroelastic MRE.

Likun Tan, Matthew D J McGarry, Elijah E W Van Houten, Ming Ji, Ligin Solamen, Wei Zeng, John B Weaver, Keith D Paulsen

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Hydraulic conductivity of human cancer tissue: A hybrid study.Bioengineering & translational medicine · 2024
    Article
  5. Transversely-isotropic brain in vivo MR elastography with anisotropic damping.Journal of the mechanical behavior of biomedical materials · 2023
    Article
  6. MR elastography: Principles, guidelines, and terminology.Magnetic resonance in medicine · 2021
    Review
  7. Article
  8. Nonlinear Inversion MR Elastography With Low-Frequency Actuation.IEEE transactions on medical imaging · 2020
    Article
  9. 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

8 authors.

Likun TanThayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America.ORCID http://orcid.org/0000-0002-2134-5441
Matthew D J McGarryDepartment of Biomedical Engineering, Columbia University, New York, NY 10027, United States of America.
Elijah E W Van HoutenDepartment of Mechanical Engineering, University de Sherbrooke, Sherbrooke, Quebec J1K 2R1, Canada.
Ming JiDepartment of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States of America.
Ligin SolamenThayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America.
Wei ZengThayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America.
John B WeaverThayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America.
Keith D PaulsenThayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America.

Funding

Spectrally optimized, Spatially resolved Poro and Viscoelastic Brain MRER01EB018230 · NIBIB · DARTMOUTH COLLEGE · PI PAULSEN, KEITH D. · 2013 to 2016
$1.4M
NIBIB NIH HHS R01 EB018230
6 · The paper itself

Abstract

A numerical framework for interstitial fluid pressure imaging (IFPI) in biphasic materials is investigated based on three-dimensional nonlinear finite element poroelastic inversion. The objective is to reconstruct the time-harmonic pore-pressure field from tissue excitation in addition to the elastic parameters commonly associated with magnetic resonance elastography (MRE). The unknown pressure boundary conditions (PBCs) are estimated using the available full-volume displacement data from MRE. A subzone-based nonlinear inversion (NLI) technique is then used to update mechanical and hydrodynamical properties, given the appropriate subzone PBCs, by solving a pressure forward problem (PFP). The algorithm was evaluated on a single-inclusion phantom in which the elastic property and hydraulic conductivity images were recovered. Pressure field and material property estimates had spatial distributions reflecting their true counterparts in the phantom geometry with RMS errors around 20% for cases with 5% noise, but degraded significantly in both spatial distribution and property values for noise levels > 10%. When both shear moduli and hydraulic conductivity were estimated along with the pressure field, property value error rates were as high as 58%, 85% and 32% for the three quantities, respectively, and their spatial distributions were more distorted. Opportunities for improving the algorithm are discussed.

Indexed as

AlgorithmsElasticity Imaging TechniquesExtracellular FluidFinite Element AnalysisHumansImage Processing, Computer-AssistedPhantoms, ImagingPressure

Identifiers

PMID28586393
PMCPMC5460821

What OpenQuestion holds

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LicenceCC BY
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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.