Abstract T P41: Combining Conventional MRI-DWI With Advanced Quantitative T2 Imaging For Characterizing Extent Of Infarction In Ischemic Stroke: The Heterogeneity Of The Evolving Ischemic Lesion
Bibliographic record
Abstract
Background: Cerebral ischemia causes cellular injury, involving not only neurons, but also cells of the neurovascular unit. DWI provides sensitive detection of cytotoxic injury during the early stages of stroke onset; however, measurement of free-water with quantitative T2 (qT2) is generally overlooked, and may provide important additional information about tissue properties not appreciated with DWI and ADC alone. Therefore, better characterization of the ischemic injury is required, particularly in the context of future stroke clinical trials. Methods: 21 ischemic stroke patients were imaged within 6 hours of symptom onset and at 24 hours on a 3T MRI. qT2 mapping was performed using a CPMG sequence. DWI was also performed to provide ADC maps. Volumetric analysis was performed on DWI, FLAIR images, ADC and T2 maps using a combined manual and threshold-based approach. Results: Ischemic lesions were identified in 18/21 (86%) patients. tPA and/or endovascular treatment was administered in 83% of these patients (recanalization confirmed in 73%). Lesions were detectable at baseline in 16 (89%) of ADC maps, 17 (94%) of qT2 maps and 11 (61%) of FLAIR images; ADC lesions grew by a median of 112% while qT2 lesions grew by 54%. The median ADC/qT2 lesion overlap grew by 16%. qT2 lesions were larger than ADC in 72% of patients at baseline, and 67% of patients at 24 hrs. Conclusions: By combining conventional DWI and advanced qT2 imaging, we are able to better characterize the heterogeneity of infarct evolution. qT2 depicts a lesion size that is often larger than depicted by ADC, which may be important for defining lesion extent. DWI and qT2 are complementary sequences that are necessary in understanding stroke injury; reliance on one parameter alone would underestimate lesion extent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".