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Record W2023992196 · doi:10.1055/s-0029-1202942

In Vivo 7.0-Tesla Magnetic Resonance Imaging of the Wrist and Hand: Technical Aspects and Applications

2009· article· en· W2023992196 on OpenAlexaff
Klaus Friedrich, Gregory Chang, Renata Macedo Calderaro Vieira, Ligong Wang, Graham C. Wiggins, Mark E. Schweitzer, Ravinder R. Regatte

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2009
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsOttawa Hospital
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of HealthMax Kade Foundation
KeywordsMagnetic resonance imagingMedicineWristImaging phantomNuclear magnetic resonancePulse sequenceMagnetic resonance spectroscopic imagingRadiofrequency coilImage resolutionSignal-to-noise ratio (imaging)Biomedical engineeringSpecific absorption rateNuclear medicineArtificial intelligenceRadiologyComputer sciencePhysicsOptics

Abstract

fetched live from OpenAlex

Magnetic resonance imaging (MRI) at 7.0 T has the potential for higher signal-to-noise ratio (SNR), improved spectral resolution, and faster imaging compared with 1.5-T and 3.0-T MR systems. This is especially interesting for challenging imaging regions like the wrist and the hand because of the small size of the visualized anatomical structures; the increase in SNR could then be directly converted into higher spatial resolution of the images. Practically, imaging at 7.0 T poses a variety of technical challenges such as static (B (0)) and radiofrequency (B (1)) homogeneities, shimming, chemical shift artifacts, susceptibility artifacts, alterations in tissue contrast, specific absorption rate limitations, coil construction, and pulse sequence tuning. Despite these limitations, this first experience in anatomical imaging of the wrist and the hand at 7.0 T is very promising. Functional imaging techniques will gain importance at ultra-high-field MRI and need to be assessed in detail in the future.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.355

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 studies0.0000.001
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 head0.004
GPT teacher head0.284
Teacher spread0.280 · 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 designObservational
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

Citations35
Published2009
Admission routes1
Has abstractyes

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