MRI-Based Classification of Cerebral Hemodynamic Failure With Resting Perfusion Metrics and Cerebrovascular Reactivity
Bibliographic record
Abstract
BACKGROUND: The inability to augment regional cerebral blood flow (CBF) in the setting of steno-occlusive disease involving large brain-supplying arteries is a risk factor for stroke. The gold standard for detecting such hemodynamic impairment requires the application of a vasodilatory stimulus while measuring changes in CBF. Resting blood flow metrics derived from computed tomography or magnetic resonance imaging (MRI) perfusion methods have been applied as surrogates for assessing hemodynamic insufficiency including relative CBF, relative cerebral blood volume, and mean transit time (MTT). The purpose of this study, therefore, was to compare the sensitivity and specificity of MRI resting perfusion metrics with cerebrovascular reactivity (CVR). MRI CVR mapping was used as a reference standard for comparing MRI perfusion–derived CBF, CBV, and MTT. METHODS: MRI resting perfusion metrics were measured using a recently reported noninvasive method that induces a bolus of hypoxia-induced deoxyhemoglobin as the dynamic susceptibility contrast in place of the gadolinium-based contrast agents. CVR was measured using a standardized hypercapnic vasoactive stimulus during blood oxygen level–dependent MRI as a surrogate for CBF. RESULTS: Twenty-two patients with large artery steno-occlusive disease (mean age±SD, 46±17.8 years; 60% female), 24 healthy participants for the CVR atlas (35.1±13.8 years; 33% female), and 25 for the perfusion atlas (38.4±17.6 years; 24% female) were recruited. Significant differences in mean hemispheric middle cerebral artery perfusion (MTT, relative CBF) and CVR metrics in gray matter ( P <0.05) were observed between patients and healthy participants. Comparisons between affected and unaffected hemispheres in patients showed significant differences only for MTT and CVR in gray matter ( P <0.05). Receiver-operating characteristic curves identified CVR as the most sensitive predictor for hemodynamic impairment followed by MTT. CONCLUSIONS: CVR remains a more accurate test for assessing hemodynamic impairment compared to resting blood flow metrics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".