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Record W4403210040 · doi:10.1109/jerm.2024.3465354

Size-Adaptive Occipital 18-Channel Receive-Only RF Coil for 3T MRI

2024· article· en· W4403210040 on OpenAlexafffund
William Mathieu, Milica Popović, Reza Farivar

Bibliographic record

VenueIEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChannel (broadcasting)Electromagnetic coilNuclear magnetic resonancePhysicsComputer scienceElectrical engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

The performance of a conformal occipital receive-only radio-frequency (RF) array is demonstrated at 3T. The ultimate aim of this larger coil is to improve whole-brain magnetic resonance imaging (MRI) regardless of a person's head size and shape. The occipital array contains 18-channels built on a 3D-printed 3-mm thick thermoplastic polyurethane (TPU) plate, which acts as a flexible substrate. To show the performance improvements of our design a comparative study was performed where three differently shaped phantoms were used when imaging by our occipital array then by a standard rigid 64-channel head product coil (posterior 40-channel section only). Signal-to-noise-ratio (SNR) and noise correlation performance were evaluated. Compared to the product coil, the flexible occipital array improved mean SNR by 2.8×. Noise correlation was comparable to the product coil. These results lead us to conclude that our design represents a viable approach to improve SNR for differently shaped heads and supports the feasibility of a larger 128-channel size-adaptable whole-head array currently in development.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.469

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 head0.036
GPT teacher head0.359
Teacher spread0.322 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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