Spatial Resolution and the Magnitude of Infarct Volume Measurement Error in DWI in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Infarct volume in acute ischemic stroke is an important prognostic marker and determines endovascular treatment decisions. This study evaluates the magnitude and potential clinical impact of the error related to partial volume effects in infarct volume measurement on diffusion-weighted MR imaging in acute stroke and explores how increasing spatial resolution could reduce this error. MATERIALS AND METHODS: Diffusion-weighted imaging of 393 patients with acute stroke, of whom 56 had anterior circulation large-vessel occlusion, was coregistered to standard space. Lesion boundaries were manually segmented. A 3D lesion-volume model was resampled for voxel sizes from 4 × 4 × 8 to 1 × 1 × 2 mm, and the surface-volume, corresponding to the partial volume error, was calculated. The number of cases with anterior circulation large-vessel occlusion, in which the endovascular therapy core threshold of 70 mL was contained within the margin of error, was calculated as a function of imaging resolution. RESULTS: The mean infarct core volume was 27.2 ± 49.9 mL. The mean surface volume was 14.7 ± 20.8 mL for 2 × 2 × 4 mm resolution and 7.4 ± 10.7 mL for 1 × 1 × 2 mm resolution. With a resolution of 2 × 2 × 4 mm, 70 mL was contained within the margin of error in 7/56 cases (12.5%) with large-vessel occlusion, while with a 1 × 1 × 2 mm voxel size, the margin of error was 3/56 (5%). The lesion-volume range of potentially misclassified lesions dropped from 46.5-94.1 mL for a 2 × 2 × 4 mm resolution to 64.4-80.1 mL for a 1 × 1 × 2 mm resolution. CONCLUSIONS: Partial volume effect is an important source of error in infarct volume measurement in acute stroke. Increasing spatial resolution substantially decreases the mean error. Standard use of high-resolution DWI should be considered to increase the reliability of infarct volume measurements.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".