Acute Ischemic Stroke: Accuracy of Diffusion-weighted MR Imaging—Effects of<i>b</i>Value and Cerebrospinal Fluid Suppression
Bibliographic record
Abstract
PURPOSE: To prospectively determine which diffusion-weighted magnetic resonance (MR) imaging technique (ie, conventional diffusion-weighted MR imaging [b = 1000 or 1500 sec/mm2] or fluid-inversion prepared diffusion [FLIPD] MR imaging [b = 1500 sec/mm2]) is most accurate in depicting acute ischemic stroke at 3 T. MATERIALS AND METHODS: The Health Research Ethics Board approved this study; written informed consent was provided by all participants or their surrogate. Diffusion-weighted MR imaging was performed in 75 consecutive patients (43 men, 32 women; mean age, 64.0 years) with acute ischemic stroke. Two experienced neuroradiologists determined the presence of hyperacute stroke lesions at diffusion-weighted MR imaging by locating areas of hyperintensity that corresponded to regions with a decreased diffusion coefficient. These findings were used as the reference standard. Four raters who were blinded to patient history assessed all images and apparent diffusion coefficient maps for the presence of changes that were consistent with acute ischemic stroke. Accuracy, sensitivity, specificity, negative predictive value, positive predictive value, and inter- and intrarater reliability scores were calculated for each technique. RESULTS: Specificity, positive predictive value, and accuracy were not significantly different among the techniques. FLIPD MR images obtained with a b value of 1500 sec/mm2 had decreased sensitivity for acute ischemic stroke (mean, 61.8%; 95% confidence interval [CI]: 55.4%, 67.9%) compared with conventional diffusion-weighted MR images obtained with a b value of either 1000 sec/mm2 (mean, 82.5%; 95% CI: 77.1%, 87.0%) or 1500 sec/mm2 (mean, 84.5%; 95% CI: 79.3%, 88.9%). FLIPD MR images also had decreased negative predictive value (mean, 96.5%; 95% CI: 95.7%, 97.2%) compared with conventional diffusion-weighted MR images obtained with a b value of either 1000 sec/mm2 (mean, 98.4%; 95% CI: 97.8%, 98.8%) or 1500 sec/mm2 (mean, 98.6%; 95% CI: 98.1%, 99.0%). Intra- and interrater reliability scores were generally excellent for all three techniques. CONCLUSION: FLIPD MR images obtained with a b value of 1500 sec/mm2 are less suitable for the detection of acute ischemic stroke owing to a decreased sensitivity and negative predictive value. The performance of the two conventional diffusion-weighted MR imaging techniques (b = 1000 and 1500 sec/mm2) was equivalent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| 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".