Penumbra Detection using PWI/DWI Mismatch MRI in a Rat Stroke Model with and without Comorbidity: Comparison of Methods
Bibliographic record
Abstract
Perfusion-diffusion (perfusion-weighted imaging (PWI)/diffusion-weighted imaging (DWI)) mismatch is used to identify penumbra in acute stroke. However, limitations in penumbra detection with mismatch are recognized, with a lack of consensus on thresholds, quantification and validation of mismatch. We determined perfusion and diffusion thresholds from final infarct in the clinically relevant spontaneously hypertensive stroke-prone (SHRSP) rat and its normotensive control strain, Wistar-Kyoto (WKY) and compared three methods for penumbra calculation. After permanent middle cerebral artery occlusion (MCAO) (WKY n=12, SHRSP n=15), diffusion-weighted (DWI) and perfusion-weighted (PWI) images were obtained for 4 hours post stroke and final infarct determined at 24 hours on T(2) scans. The PWI/DWI mismatch was calculated from volumetric assessment (perfusion deficit volume minus apparent diffusion coefficient (ADC)-defined lesion volume) or spatial assessment of mismatch area on each coronal slice. The ADC-derived lesion growth provided the third, retrospective measure of penumbra. At 1 hour after MCAO, volumetric mismatch detected smaller volumes of penumbra in both strains (SHRSP: 31 ± 50 mm(3), WKY: 22 ± 59 mm(3), mean ± s.d.) compared with spatial assessment (SHRSP: 36 ± 15 mm(3), WKY: 43 ± 43 mm(3)) and ADC lesion expansion (SHRSP: 41 ± 45 mm(3), WKY: 65 ± 41 mm(3)), although these differences were not statistically significant. Spatial assessment appears most informative, using both diffusion and perfusion data, eliminating the influence of negative mismatch and allowing the anatomical location of penumbra to be assessed at given time points after stroke.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".