Cerebral OEF quantification: A comparison study between quantitative susceptibility mapping and dual‐gas calibrated BOLD imaging
Bibliographic record
Abstract
Purpose To compare regional oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen consumption (CMRO 2 ) quantified from the microvascular quantitative susceptibility mapping (QSM) using a hypercapnic gas challenge with those measured by the dual‐gas calibrated BOLD imaging (DGC‐BOLD) in healthy subjects. Methods Ten healthy subjects were scanned using a 3T MR system. The QSM data were acquired with a multi‐echo gradient‐echo sequence at baseline and hypercapnia. Cerebral blood flow data were acquired using the pseudo‐continuous arterial spin labeling technique. Baseline OEF and CMRO 2 were calculated using QSM and cerebral blood flow measurements. The DGC‐BOLD data were also collected under a hypercapnic and a hyperoxic condition to yield baseline OEF and CMRO 2 . The QSM‐OEF and CMRO 2 maps were compared with DGC‐BOLD OEF and CMRO 2 maps using region of interest (vascular territories) analysis and Bland‐Altman plots. Results Hypercapnia is a robust stimulus for mapping OEF in combination with QSM. Average OEF in 16 vascular territory regions of interest across 10 subjects was 0.40 ± 0.04 by QSM‐OEF and 0.38 ± 0.09 by DGC‐BOLD. The average CMRO 2 was 176 ± 35 and 167 ± 53 μmol O 2 /min/100g by QSM‐OEF and DGC‐BOLD, respectively. A Bland‐Altman plot of regional OEF and CMRO 2 in regions of interest revealed a statistically significant but small difference (OEF difference = 0.02, CMRO 2 difference = 9 μmol O 2 /min/100g, p < .05) between the 2 methods for the 10 healthy subjects. Conclusion Hypercapnic challenge–assisted QSM‐OEF is a feasible approach to quantify regional brain OEF and CMRO 2 . Compared with DGC‐BOLD, hypercapnia QSM‐OEF results in smaller intersubject variability and requires only 1 gas challenge.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".