Abstract WMP24: Quantitative Relaxometry Quantifies Brain Edema After Ischemic Stroke
Bibliographic record
Abstract
Introduction: Brain edema is an adverse complication of ischemic stroke, and is associated with substantial morbidity and mortality. We investigated whether relaxometry parameters of MRI are a reliable measure of brain edema in an animal model. Hypothesis: We hypothesize that quantitative relaxometry parameters of MRI in a rat model of stroke tightly correlate with brain edema. Methods: We permanently occluded the middle cerebral artery of 18 rats using the filament occlusion method. Fifteen surviving animals were imaged at 48 hours with a Bruker 4.7 T MRI scanner with Diffusion-weighted imaging (DWI), T1 and T2 maps, and proton-density weighted (PDW) imaging. Hemispheric and lesional volumes were generated on DWI. For quantitative T1, quantitative T2 and PDW images, signal intensity values relative to the contralateral hemisphere were determined. The percent water content in the rat brain was measured using the wet-dry method. Additional volumetric measurements of swelling were calculated based on hemisphere volumes determined on MRI. Correlation testing and logistic regression was performed to assess the relationship between imaging measures and swelling. Results: The mean lesion volume was 352 mm3. Brain water content and swelling volume were closely associated (r=0.80, p<0.001). PDW, T1 and T2 ratios highly correlated with brain water content (r=0.91, p<0.0001, r=0.94, p<0.0001 and r=0.97, p<0.0001, respectively). Ratios for PDW, T1 and T2 were also associated with swelling volume (r=0.67, p<0.0063, r=0.73, p<0.0022, and r=0.74, p<0.0017). Conclusion: Signal intensity ratios derived from PDW as well as quantitative T1 and T2 MRI can be leveraged to quantify brain water content and brain edema. These measures are useful markers for edema quantification that can be applied to any condition that leads to brain edema in both animal models and human patients.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".