Quantification of brain oxygen extraction fraction using QSM and a hyperoxic challenge
Bibliographic record
Abstract
Purpose To use hyperoxia in combination with QSM to quantify microvascular oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO 2 ) in healthy subjects and to cross‐validate results with those from hypercapnia QSM‐OEF. Methods Ten healthy subjects were scanned on a 3T MRI scanner. At baseline normoxia and during hyperoxia (PetO 2 = +300 mmHg), QSM data were acquired using a multi‐echo gradient‐echo (GRE) sequence, and cerebral blood flow data were acquired using a pseudocontinuous arterial spin labeling sequence. The OEF and CMRO 2 maps were computed and compared with those from hypercapnia QSM‐OEF, acquired in the same subjects, using correlation and Bland‐Altman analysis in 16 vascular territories. Results Hyperoxia QSM‐OEF produced physiologically reasonable OEF and CMRO 2 values in all subjects (gray‐matter region of interest average OEF = 0.42 ± 0.04, average CMRO 2 = 181 ± 34 μmol O 2 /min/100 g). When compared with hypercapnia QSM‐OEF, Bland‐Altman plots revealed small deviations (mean OEF difference = 0.015, mean CMRO 2 difference = 4.9 μmol O 2 /min/100 g, P < .05). Good and excellent correlations of regional OEF and CMRO 2 were found for the two methods. In addition, hyperoxia had minimal impact on cerebral blood flow (average gray‐matter cerebral blood flow was reduced by 7.5 ± 5.4%). Conclusions Hyperoxia in combination with QSM is a robust approach to measure OEF. Compared with hypercapnia, hyperoxia is more comfortable and has minimal impact on cerebral blood flow.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".