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Record W2045048217 · doi:10.1002/jmri.20685

Adaptive bilateral breast MRI using projection reconstruction time‐resolved imaging of contrast kinetics

2006· article· en· W2045048217 on OpenAlexaff
Elizabeth Ramsay, Petrina A. Causer, Kylie Hill, Donald B. Plewes

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2006
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsDynamic contrast-enhanced MRIBreast MRIDynamic contrastImage qualityProjection (relational algebra)Dynamic imagingContrast (vision)Cartesian coordinate systemSagittal planeComputer scienceReal-time MRIBreast imagingTemporal resolutionImage resolutionIterative reconstructionNuclear medicineComputer visionMagnetic resonance imagingMedicineRadiologyImage processingMathematicsBreast cancerPhysicsImage (mathematics)MammographyAlgorithmOpticsDigital image processing

Abstract

fetched live from OpenAlex

PURPOSE: To introduce a bilateral implementation of an adaptive imaging technique in which both dynamic and high resolution breast MR images are acquired simultaneously. MATERIALS AND METHODS: Adaptive three-dimensional bilateral breast imaging in the sagittal plane was achieved by combining two elements: a projection reconstruction time-resolved imaging of contrast kinetics (PR-TRICKS) k-space trajectory and a slab interleaved sequence that imaged alternate breasts every TR. A pilot study was performed to evaluate image quality and contrast uptake behavior, using eight patients with previously identified benign lesions. RESULTS: Adaptive reconstruction demonstrated breast lesions in all eight women with similar image quality and signal-to-noise ratio (SNR) to Cartesian images with comparable imaging parameters. Contrast enhancement curves covering the entire postinjection time period were obtained from the dynamic images and in one case compared to previous enhancement profiles from a conventional Cartesian trajectory. CONCLUSION: Bilateral dynamic and high spatial resolution images with high SNR can be achieved in a clinically feasible manner, providing both kinetic and morphologic analysis with a single data set. This may obviate the need for multiple MRI examinations for a thorough breast MRI workup.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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

Citations19
Published2006
Admission routes1
Has abstractyes

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