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Record W2097471609 · doi:10.1002/mrm.20393

Oxygen‐sensitive contrast in blood for steady‐state free precession imaging

2005· article· en· W2097471609 on OpenAlexafffund
Rohan Dharmakumar, Juimiin Hong, Jean H. Brittain, Donald B. Plewes, Graham A. Wright

Bibliographic record

VenueMagnetic Resonance in Medicine · 2005
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Institutes of Health ResearchTerry Fox Foundation
KeywordsSteady-state free precession imagingContrast (vision)OxygenNuclear magnetic resonanceBlood-oxygen-level dependentPrecessionSteady state (chemistry)ChemistryMagnetic resonance imagingPhysicsOpticsMedicineRadiology

Abstract

fetched live from OpenAlex

Steady-state free precession (SSFP) methods have gained widespread recognition for their ability to provide fast scans at high signal-to-noise ratio. This paper demonstrates that such methods are also capable of reflecting functional information, particularly blood oxygenation state. It is well known that SSFP signals show substantial sensitivity to small off-resonance frequency variations. However, that mechanism cannot explain the oxygen-sensitive contrast in blood that was observed with steady-state methods using phase-cycled radiofrequency pulses. From theoretical and experimental models it is demonstrated that the mechanism responsible for such contrast originates from the motion of spins through local field inhomogeneities in and around deoxygenated red blood cells. In addition, this work shows that it is critical to choose the scan parameters carefully for robust oxygen-sensitive contrast. Finally, it is demonstrated that it is possible to build a quantitative model that incorporates the Luz-Meiboom model, which had been used in the past to estimate quantitative measures of vascular blood oxygen levels. It is envisioned that this method could be instrumental in real-time imaging focused on detecting diseases where the oxygen state of blood is impaired.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.638

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 head0.013
GPT teacher head0.321
Teacher spread0.308 · 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
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

Citations60
Published2005
Admission routes2
Has abstractyes

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