Evidence map›Paper›PMID 37067891›Full record

ArticleComputational biomechanics for medicine. Personalisation, validation and therapy2020

Quantitative Validation of MRI-Based Motion Estimation for Brain Impact Biomechanics.

Arnold D Gomez, Andrew K Knutsen, Dzung L Pham, Philip V Bayly, Jerry L Prince

Open access · greenAbstract read
In one paragraph

Article in Computational biomechanics for medicine. Personalisation, validation and therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.9field-weighted citation impact, top 18% of its field
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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

5 authors at 4 institutions in 1 country.

Arnold D GomezDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA.
Andrew K KnutsenCenter for Neuroscience and Regenerative Medicine, Henry Jackson Foundation, Bethesda, USA.
Dzung L PhamCenter for Neuroscience and Regenerative Medicine, Henry Jackson Foundation, Bethesda, USA.
Philip V BaylyMechanical Engineering Department, Washington University in St. Louis, St. Louis, USA.
Jerry L PrinceDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA.
Johns Hopkins University · USCenter for Neuroscience and Regenerative Medicine · USJackson Foundation · USWashington University in St. Louis · US

Funding

IN VIVO MEASUREMENT OF BRAIN BIOMECHANICSR01NS055951 · NINDS · WASHINGTON UNIVERSITY · PI BAYLY, PHILIP V · 2007 to 2017
$3.9M
Longitudinal Sub-thalamic Structure and Functional Alterations in Mild Traumatic Brain InjuryR01NS105503 · NINDS · UNIVERSITY OF MARYLAND BALTIMORE · PI BADJATIA, NEERAJ, PRINCE, JERRY L · 2018 to 2022
$3.5M
Tongue muscle function after cancer surgery using 4D MRI, DTI, and MR taggingR01DC014717 · NIDCD · JOHNS HOPKINS UNIVERSITY · PI PRINCE, JERRY L · 2015 to 2019
$1.7M
NIDCD NIH HHS R01 DC014717NINDS NIH HHS R01 NS055951NINDS NIH HHS R01 NS105503
6 · The paper itself

Abstract

Head impact can cause traumatic brain injury (TBI) through axonal overstretch or subsequent inflammation and understanding the biomechanics of the impact event is useful for TBI prevention research. Tagged magnetic resonance imaging (MRI) acquired during a mild-acceleration impact has enabled measurement and visualization of brain deformation in vivo. However, measurements using MRI are subject to error, and having independent validation while imaging in vivo is very difficult. Thus, characterizing the accuracy of these measurements needs to be done in a separate experiment using a phantom where a gold standard is available. This study describes a method for error quantification using a calibration phantom compatible with MRI and high-speed video (the gold standard). During linear acceleration, the maximum shear strain (MSS) in the phantom ranged from 0 to 12%, which is similar to in vivo brain deformation at a similar acceleration. The mean displacement error against video was 0.3±0.3 mm, and the MSS error was 1.4±0.3%. To match resolutions, video data was filtered temporally using an averaging filter. Compared to the unfiltered results, resolution matching improved the agreement between MRI and video results by 15%. In conclusion, tagged MRI analysis compares well to video data provided that resolutions are matched-a finding that is also applicable when using MRI to validate simulations.

Identifiers

PMID37067891
PMCPMC10103905
OpenAlexW2967115751

What OpenQuestion holds

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Registered trials

None linked

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.