Evidence map›Paper›PMID 35073998›Full record

ArticleBMC biomedical engineering2022

Development of computational models for microtesla-level magnetic brain scanning: a novel avenue for device development.

Shane Shahrestani, Gabriel Zada, Yu-Chong Tai

Open access · goldAbstract read
In one paragraph

Article in BMC biomedical engineering, 2022. 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
0.1field-weighted citation impact, top 57% 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, 1 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

3 authors at 2 institutions in 1 country.

Shane ShahrestaniDepartment of Neurological Surgery, Keck School of Medicine, University of Southern California, CA, Los Angeles, USA. sshahres@caltech.edu.ORCID http://orcid.org/0000-0001-7561-4590
Gabriel ZadaDepartment of Neurological Surgery, Keck School of Medicine, University of Southern California, CA, Los Angeles, USA.
Yu-Chong TaiDepartment of Medical Engineering, California Institute of Technology, CA, Pasadena, USA.
University of Southern California · USCalifornia Institute of Technology · US

Funding

Mentored Career DevelopmentKL2TR001854 · NCATS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Elizabeth Rhea Erwin Burner, Amy Elizabeth West · 2016 to 2026
$9.0M
NCATS NIH HHS KL2 TR001854
6 · The paper itself

Abstract

backgroundDetection of locally increased blood concentration and perfusion is critical for assessment of functional cortical activity as well as diagnosis of conditions such as intracerebral hemorrhage (ICH). Current paradigms for assessment of regional blood concentration in the brain rely on computed tomography (CT), magnetic resonance imaging (MRI), and perfusion blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI).

resultsIn this study, we developed computational models to test the feasibility of novel magnetic sensors capable of detecting hemodynamic changes within the brain on a microtesla-level. We show that low-field magnetic sensors can accurately detect changes in magnetic flux density and eddy current damping signals resulting from increases in local blood concentration. These models predicted that blood volume changes as small as 1.26 mL may be resolved by the sensors, implying potential use for diagnosis of ICH and assessment of regional blood flow as a proxy for cerebral metabolism and neuronal activity. We then translated findings from our computational model to demonstrate feasibility of accurate detection of modeled ICH in a simulated human cadaver setting.

conclusionsOverall, microtesla-level magnetic scanning is feasible, safe, and has distinct advantages compared to current standards of care. Computational modeling may facilitate rapid prototype development and testing of novel medical devices with minimal risk to human participants prior to device construction and clinical trials.

Indexed as

BrainComputationalCOMSOL multiphysicsEddy current dampingHemorrhageMicroteslaModelingNeurologyStroke

Identifiers

PMID35073998
PMCPMC8785482
OpenAlexW4207063477

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.