MétaCan
Menu
Back to cohort
Record W2041977918 · doi:10.1148/rg.295095041

MR Imaging at 3.0 T in Children: Technical Differences, Safety Issues, and Initial Experience

2009· review· en· W2041977918 on OpenAlexaff
Govind B. Chavhan, Paul Babyn, Manoj Singh, Logi Vidarsson, Manohar Shroff

Bibliographic record

VenueRadiographics · 2009
Typereview
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineMagnetic resonance imagingImage qualitySignal-to-noise ratio (imaging)Field of viewSpecific absorption rateNoise (video)Computer visionNuclear medicineRadiologyImage (mathematics)Computer scienceTelecommunications

Abstract

fetched live from OpenAlex

The high signal-to-noise ratio and contrast-to-noise ratio of 3.0-T magnetic resonance (MR) imaging can be used to obtain high-resolution thin-section images in a short acquisition time. These advantages are associated with an increased specific absorption rate (SAR) and more artifacts owing to B(1) inhomogeneity and increased susceptibility and chemical shift. Potential advantages of 3-T imaging in children include acquisition of good-quality images even with a small field of view (FOV). The shorter overall acquisition time of 3-T imaging is useful in children, who may not be able to cooperate for long. Shorter acquisition times also improve safety by reducing patient monitoring time within the enclosed bore of an MR imaging unit. SAR-related issues and dielectric artifacts are less problematic with a small FOV. Parallel imaging helps reduce SAR, susceptibility artifacts, and blurring of T2-weighted fast spin-echo (FSE) and single-shot FSE images by reducing the echo train length.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.383
Teacher spread0.353 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations44
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueRadiographicsSame topicAdvanced MRI Techniques and ApplicationsFrench-language works237,207