MétaCan
Menu
Back to cohort
Record W2493016151 · doi:10.1002/mrm.26354

A 24‐channel shim array for the human spinal cord: Design, evaluation, and application

2016· article· en· W2493016151 on OpenAlexafffund
Ryan Topfer, Piotr M. Starewicz, Kai‐Ming Lo, Karl Metzemaekers, Donald Jette, Hoby P. Hetherington, Nikola Stikov, Julien Cohen‐Adad

Bibliographic record

VenueMagnetic Resonance in Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversité de MontréalMontreal Heart InstitutePolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsShim (computing)Imaging phantomMagnetic resonance imagingSpinal cordBiomedical engineeringComputer scienceMaterials scienceNuclear medicineMedicineRadiologySurgery

Abstract

fetched live from OpenAlex

PURPOSE: A novel multichannel shim array is introduced to improve MRI and spectroscopic studies of the human spinal cord. METHODS: Twenty-four-channel shim and 8-channel transceiver arrays were designed to insert into the patient bed table to lie in close proximity to the subject's spine. The reference field patterns of each of the shim channels (Hz/A) were determined empirically via gradient echo field mapping and subsequently used to demonstrate shim performance at 3 Tesla using an ex vivo phantom, which incorporated a fixed human spine. The shim was further demonstrated on five healthy volunteers. RESULTS: ) from an original 51.3 Hz down to 32.5 Hz, amounting to an improvement in field homogeneity of 36.6%. In vivo, the spine shim resulted in an average improvement in field homogeneity of 63.8 ± 15.4%. CONCLUSION: The localized spine shim offers a promising new means of correcting magnetic field distortion in the spinal cord. Magn Reson Med 76:1604-1611, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.072
GPT teacher head0.398
Teacher spread0.326 · 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 designOther design
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

Citations35
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueMagnetic Resonance in MedicineSame topicAdvanced MRI Techniques and ApplicationsFrench-language works237,207