Susceptibility-Weighted Imaging is More Reliable Than T2*-Weighted Gradient-Recalled Echo MRI for Detecting Microbleeds
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We investigated the sensitivity and reliability of MRI susceptibility-weighted imaging (SWI) compared with routine MRI T2*-weighted gradient-recalled echo (GRE) for cerebral microbleed (CMB) detection. METHODS: We used data from a prospective study of cerebral amyloid angiopathy (n=9; mean age, 71±8.3) and healthy non-cerebral amyloid angiopathy controls (n=22; mean age, 68±6.3). Three raters (labeled 1, 2, and 3) independently interpreted the GRE and SWI sequences (using the phase-filtered magnitude image) blinded to clinical information. RESULTS: In 9 cerebral amyloid angiopathy cases, the raters identified 1146 total CMBs on GRE and 1432 CMBs on SWI. In 22 healthy control subjects, the raters identified ≥1 CMBs in 6/22 on GRE (total 9 CMBs) and 5/22 on SWI (total 19 CMBs). Among cerebral amyloid angiopathy cases, the reliability between raters for CMB counts was good for SWI (intraclass correlation coefficient, 0.87) but only moderate for GRE (intraclass correlation coefficient, 0.52). In controls, agreement on the presence or absence of CMBs in controls was moderate to good on both SWI (κ coefficient ranged from 0.57 to 0.74 across the 3 combinations of rater pairs) and GRE (κ range, 0.31 to 0.70). A review of 114 hypointensities identified as possible CMBs indicated that increased detection and reliability on SWI was related to both increased contrast and higher resolution, allowing better discrimination of CMBs from the background and better anatomic differentiation from pial vessels. CONCLUSIONS: SWI confers greater reliability as well as greater sensitivity for CMB detection compared with GRE, and should be the preferred sequence for quantifying CMB counts.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".