Oxygen‐sensitive contrast in blood for steady‐state free precession imaging
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".