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Record W4206415431 · doi:10.1109/jerm.2021.3136090

Towards Brain MRI Adaptable to Head Size: Bowing RF Coil Phased Arrays

2021· article· en· W4206415431 on OpenAlexaff
William Mathieu, Milica Popović, Reza Farivar

Bibliographic record

VenueIEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology · 2021
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsBowingPhased arrayElectromagnetic coilHead (geology)AcousticsNoise (video)EngineeringComputer sciencePhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Purpose: Bowing phased arrays allow for the idea of hybrid rigid-flexible RF coils, in which parts of a larger phased array are broken up into rigid sub-arrays, connected by flexible coil elements, permitting coils to expend or shrink to match different human head sizes. Methods: two arrays were constructed, a conventional rigid array used as a control and an array composed of flexible bowing elements. Bowing elements comprise of a flexible half and a rigid half. The arrays were then supported on translating boards connected so that a flexible gap between the boards corresponds to the bowing elements. The gap between the rigid coil elements was varied by discrete values to assess its performance under different bowing conditions. Results: signal-to-noise-ratio (SNR) and noise performance was compared between the two arrays. It is seen that a bowing array outperforms its rigid counterpart in terms of average SNR, max SNR, and signal coverage. We conclude that bowing array elements present a viable solution to the proposed hybrid rigid-flexible coil arrays.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.036
GPT teacher head0.370
Teacher spread0.334 · 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 designBench or experimental
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

Citations5
Published2021
Admission routes1
Has abstractyes

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