3-Tesla versus 1.5-Tesla Magnetic Resonance Diffusion and Perfusion Imaging in Hyperacute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Clinical 3-tesla magnetic resonance imaging systems are becoming widespread. No studies have examined differences between 1.5-tesla and 3-tesla imaging for the assessment of hyperacute ischemic stroke (<6 h from symptom onset). Our objective was to compare 1.5-tesla and 3-tesla diffusion and perfusion imaging for hyperacute stroke using optimized protocols. METHODS: Three patients or their surrogate provided informed consent. Diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) was performed sequentially at 1.5 T and 3 T. DWI, apparent diffusion coefficient (ADC) maps and relative time-to-peak (TTP) maps were registered and assessed. DWI contrast-to-noise ratio (CNR) and ADC contrast were measured and compared. The infarct lesion volume (ILV) and thresholded ischemic volume (TIV) were estimated on the ADC and TTP maps, respectively, with the penumbral volume being defined as the difference between these volumes. RESULTS: Qualitatively, the 3-tesla TTP images exhibited greater feature detail. Quantitatively, the DWI CNR and ILV were similar at both field strengths, the ADC contrast was greater at 3 T and the TIV and penumbral volumes were much smaller at 3 T. CONCLUSIONS: Overall, the 3-tesla diffusion and perfusion images were at least as good and in some ways superior to the 1.5-tesla images for assessing hyperacute stroke. The TTP maps showed greater feature detail at 3 T. The ischemic and penumbra volumes were much greater at 1.5 T, indicating a potential difference in the diagnostic utility of the PWI-DWI mismatch between field strengths.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".