Perfusion MRI using endogenous deoxyhemoglobin as a contrast agent: Preliminary data
Bibliographic record
Abstract
PURPOSE: To demonstrate the feasibility of mapping cerebral perfusion metrics with BOLD MRI during modulation of pulmonary venous oxygen saturation. METHODS: A gas blender with a sequential gas delivery breathing circuit was used to implement rapid isocapnic changes in the partial pressure of oxygen of the arterial blood. Partial pressure of oxygen was initially lowered to a baseline of 40 mmHg. It was then rapidly raised to 95 mmHg for 20 s before rapidly returning to baseline. The induced cerebral changes in deoxyhemoglobin concentration were tracked over time using BOLD MRI in 6 healthy subjects and 1 patient with cerebral steno-occlusive disease. BOLD signal change, contrast-to-noise ratio, and time delay metrics were calculated. Perfusion metrics such as mean transit time, relative cerebral blood volume, and relative cerebral blood flow were calculated using a parametrized method with a mono-exponential residue function. An arterial input function from within the middle cerebral artery was used to scale relative cerebral blood volume and calculate absolute cerebral blood volume and cerebral blood flow. RESULTS: In normal subjects, average gray and white matter were: BOLD change = 6.3 ± 1.2% and 2.5 ± 0.6%, contrast-to-noise ratio = 4.3 ± 1.3 and 2.6 ± 0.7, time delay = 2.3 ± 0.6 s and 3.6 ± 0.7 s, mean transit time = 3.9 ± 0.6 s and 5.5 ± 0.6 s, relative cerebral blood volume = 3.7 ± 0.9 and 1.6 ± 0.4, relative cerebral blood flow = 70.1 ± 8.3 and 20.6 ± 4.0, cerebral blood flow volume = 4.1 ± 0.9 mL/100 g and 1.8 ± 0.5 mL/100 g, and cerebral blood flow = 97.2 ± 18.7 mL/100 g/min and 28.7 ± 5.9 mL/100 g/min. CONCLUSION: This study demonstrates that induced abrupt changes in deoxyhemoglobin can function as a noninvasive vascular contrast agent that may be used for cerebral perfusion imaging.
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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 | 0.001 |
| 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 | 0.001 |
| 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 | 0.001 |
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".