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Simulation Comparison of Birdcage Coil and Metamaterial Liner for MRI at 3T and 4.7T

2021· article· en· W3128034544 on OpenAlexafffund
Adam Maunder, Nicola De Zanche, Ashwin K. Iyer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Alberta
FundersAlberta InnovatesUniversity of Alberta
KeywordsSpecific absorption rateElectromagnetic coilMetamaterialHomogeneity (statistics)Radio frequencyRadiofrequency coilMaterials scienceMagnetic resonance imagingElectromagnetic shieldingMagnetic fieldNuclear magnetic resonanceAcousticsShieldSplit-ring resonatorBiomedical engineeringPhysicsComputer scienceOptoelectronicsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

The application of a thin metamaterial (MTM) liner to the RF shield of the magnetic resonance imaging (MRI) system is proposed to improve whole-body imaging compared to conventional "birdcage" body volume coils. The MTM liner facilitates travelling wave (TW) MRI, where the radio-frequency (RF) magnetic field propagates within the MRI bore, which acts as circular waveguide. Full-wave electromagnetic simulations were performed comparing a conventional birdcage coil to a novel MTM liner design in terms of key MR RF performance metrics: transmit efficiency, homogeneity and specific absorption rate (SAR). The designs are compared at 3T (128 MHz) and 4.7T (200MHz), two high-field strengths where the RF field homogeneity and SAR constraints are critical. The ring-structure design of the MTM liner is described and simulations with an empty MRI bore and a homogeneous human body model are performed, including realistic losses for the lumped components used in practical construction. The results show that the MTM liner has a similar mean transmit efficiency, while having reduced 10g averaged local SAR compared to the conventional birdcage.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.266
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2021
Admission routes2
Has abstractyes

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