A non-invasive reference-based method for imaging the cerebral metabolic rate of oxygen by PET/MR: theory and error analysis
Bibliographic record
Abstract
Positron emission tomography (PET) remains the gold standard for quantitative imaging of the cerebral metabolic rate of oxygen (CMRO2); however, it is an invasive and complex procedure that requires accounting for recirculating [O-15]H2O (RW) and the cerebral blood volume (CBV). This study presents a non-invasive reference-based technique for imaging CMRO2 that was developed for PET/magnetic resonance imaging (MRI) with the goal of simplifying the PET procedure while maintaining its ability to quantify metabolism. The approach is to use whole-brain (WB) measurements of oxygen extraction fraction (OEF) and cerebral blood flow (CBF) to calibrate [O-15]O-2-PET data, thereby avoiding the need for invasive arterial sampling. Here we present the theoretical framework, along with error analyses, sensitivity to PET noise and inaccuracies in input parameters, and initial assessment on PET data acquired from healthy participants. Simulations showed that neglecting RW and CBV corrections caused errors in CMRO2 of less than 10% for changes in regional OEF of 25%. These predictions were supported by applying the reference-based approach to PET data, which resulted in remarkably similar CMRO2 images to those generated by analyzing the same data using a modeling approach that incorporated the arterial input functions and corrected for CBV contributions. Significant correlations were observed between regional CMRO2 values from the two techniques (slope = 1.00 0.04, R-2 > 0.98) with no significant differences found for integration times of 3 and 5 min. In summary, results demonstrate the feasibility of producing quantitative CMRO2 images by PET/MRI without the need for invasive blood sampling.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".