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A reduced field-of-view method to increase temporal resolution or reduce scan time in cine MRI

2000· article· en· W2090899057 on OpenAlexfundno aff
Bruno Madore, Jill Fredrickson, Marcus T. Alley, Norbert J. Pelc

Bibliographic record

VenueMagnetic Resonance in Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsnot available
FundersNational Center for Research ResourcesNatural Sciences and Engineering Research Council of Canada
KeywordsField of viewImage resolutionDynamic imagingResolution (logic)Temporal resolutionDynamic contrastComputer scienceSpiral (railway)PhysicsMagnetic resonance imagingNuclear medicineOpticsComputer visionArtificial intelligenceMathematicsImage (mathematics)Image processingRadiologyMedicine

Abstract

fetched live from OpenAlex

In some dynamic applications of MRI, only a part of the field-of-view (FOV) actually undergoes dynamic changes. A class of methods, called reduced-FOV (rFOV) methods, convert the knowledge that some part of the FOV is static or not very dynamic into an increase in temporal resolution for the dynamic part, or into a reduction in the scan time. Although cardiac imaging is an important example of an imaging situation where changes are concentrated in a fraction of the FOV, the rFOV methods developed up to now are not compatible with one of the most common cardiac sequences, the so-called retrospective cine method. The present work is a rFOV method designed to be compatible with cine imaging. An increase by a factor n in temporal resolution or a decrease by n in scan time is obtained in the case where only one nth of the FOV is dynamic (the rest being considered static). Results are presented for both Cartesian and spiral imaging.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.385
Teacher spread0.361 · 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
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

Citations18
Published2000
Admission routes1
Has abstractyes

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