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Record W4388703519 · doi:10.1002/nbm.5068

Detunable wireless Litzcage coil for human head MRI at 1.5 T

2023· article· en· W4388703519 on OpenAlexaff
Haoqin Zhu, Michael L. Lang, Yijin Yang, Melanie Martin, Gong Zhang, Qiang Zhang, Yuanyuan Chen, Xinqiang Yan

Bibliographic record

VenueNMR in Biomedicine · 2023
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of WinnipegManitoba Health
Fundersnot available
KeywordsElectromagnetic coilRadiofrequency coilWirelessComputer scienceRadio frequencyConductorPreamplifierElectrical engineeringElectronic engineeringNuclear magnetic resonanceAcousticsMaterials sciencePhysicsEngineeringBandwidth (computing)AmplifierTelecommunications

Abstract

fetched live from OpenAlex

Inductively coupled radiofrequency (RF) coils are an inexpensive and simple method to realize wireless RF coils in magnetic resonance imaging (MRI), which can significantly ease the MRI scan setup and improve patient comfort because they do not require bulky components such as cables, baluns, preamplifiers, and connectors. However, volume-type wireless coils are typically operated in transmit/receive mode because detuning such coils is much more challenging due to their complex structure and multiple resonant modes. Meanwhile, adding too many detuning circuits to a wireless coil would decrease the coil's quality factor, impair the signal-to-noise ratio, and increase the cost. In this work, we proposed, constructed, and tested a novel wireless volume coil based on the Litzcage design for 1.5-T head imaging. Being an inductively coupled coil, it has a much simpler structure, resulting in a lighter weight and less bulky design. Despite its simpler structure, it exhibits comparable imaging performance with a commercial receive array, providing an alternative to conventional wired coils with a high cost and complex structure. The unique figure-of-8 conductor pattern within the rungs ensures that the proposed wireless Litzcage can be efficiently detuned with minimal detuning circuits.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.052
GPT teacher head0.413
Teacher spread0.361 · 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 designNot applicable
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

Citations16
Published2023
Admission routes1
Has abstractyes

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