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Record W4292636492 · doi:10.1002/nbm.4818

Low‐cost inductively coupled stacked wireless RF coil for MRI at 3 T

2022· article· en· W4292636492 on OpenAlexaff
Ming Lu, Shuyang Chai, Haoqin Zhu, Xinqiang Yan

Bibliographic record

VenueNMR in Biomedicine · 2022
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsResearch ManitobaManitoba Health
FundersNational Natural Science Foundation of China
KeywordsElectromagnetic coilBalunWirelessRadiofrequency coilCapacitorMaterials scienceRadio frequencyComputer scienceElectronic engineeringElectrical engineeringNuclear magnetic resonancePhysicsTelecommunicationsEngineeringAntenna (radio)Voltage

Abstract

fetched live from OpenAlex

Inductively coupled RF coils are an inexpensive and simple method to realize wireless RF coils in MRI. They are low cost and can greatly ease the MR scan setup and improve patient comfort, since they do not require bulky components such as cables, baluns, preamplifiers, and connectors. Previous works have typically used single-layer loops as wireless coils. In this work, we present a novel wireless coil, where two loops are stacked and decoupled with a shared capacitor. We found that such a stacked structure could increase the coil efficiency and SNR. Compared with the single-layer wireless coil, both electromagnetic simulation and MR experiment results demonstrate that the stacked wireless coil has a considerable SNR improvement of approximately 35%.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.030
GPT teacher head0.343
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations14
Published2022
Admission routes1
Has abstractyes

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