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Record W4236482378 · doi:10.1002/jmri.22605

Supine breast MRI

2011· article· en· W4236482378 on OpenAlexaff
Peter Siegler, Claire Holloway, Petrina A. Causer, Gilbert Thevathasan, Donald B. Plewes

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2011
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsSupine positionBreast MRIMedicineBreast cancerMagnetic resonance imagingRadiologyProne positionBreast imagingMammographySurgeryCancerInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To achieve high-quality unilateral supine breast magnetic resonance imaging (MRI) as a step to facilitate image aiding of clinical applications, which are often performed in the supine position. Contrast-enhanced breast MRI is a powerful tool for the diagnosis of cancer. However, prone patient positioning typically used for breast MRI hinders its use for image aiding. MATERIALS AND METHODS: A fixture and a flexible four-element receive coil were designed for patient-specific shaping and placement of the coil in close conformity to the supine breast. A 3D spoiled gradient sequence was modified to incorporate compensation of respiratory motion. The entire setup was tested in volunteer experiments and in a pilot patient study. RESULTS: The flexible coil design and the motion compensation produced supine breast MR images of high diagnostic value. Variations in breast shape and in tissue morphology within the breast were observed between a supine and a diagnostic prone MRI of a patient. CONCLUSION: The presented supine breast MRI achieved an image quality comparable to diagnostic breast MRI. Since supine positioning is common in many clinical applications such as ultrasound-guided breast biopsy or breast-conserving surgery, the registration of the supine images will aid these applications.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.468

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 studies0.0000.000
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 head0.016
GPT teacher head0.283
Teacher spread0.266 · 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

Citations42
Published2011
Admission routes1
Has abstractyes

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