Abstract W MP119: Sensitivity and Reliability of MRI SWI Compared With GRE Sequences for Detecting Microbleeds in a Community Population
Bibliographic record
Abstract
Background: MRI susceptibility-weighted imaging (SWI) provides better contrast and is typically done at higher spatial resolution than MRI T2*-weighted gradient-recalled echo (GRE), increasing the sensitivity for cerebral microbleed (CMB) detection. However, few studies have implemented SWI and GRE in the same scan session, and there is uncertainty regarding whether findings on SWI are comparable to findings on GRE. We compared the sensitivity and reliability of SWI vs. GRE sequences for detecting CMBs in a community population. Methods: Using a standardized case report form, two radiologists independently identified CMBs on SWI and GRE in 247 participants age 40 to 75 in the community-based Prospective Urban Rural (PURE) MIND substudy. MRI was performed on the same GE 3T MRI scanner using typical clinical sequence parameters. SWI and GRE were read >2 weeks apart. After the independent readings, the two raters met to determine CMB presence by consensus. Results: Mean age was 58.0±7.6 years, 65% were women, 23% had hypertension and 8.6% had diabetes. By consensus, CMBs were seen in 30 participants (12.2%) on SWI but in only 13 (5.2%) on GRE (p<0.001). Inter-rater reliability was moderately good on SWI (kappa 0.52, 95% CI 0.34-0.71) and GRE (kappa 0.51, 95% CI 0.30-0.73). A pattern of lobar CMBs, with or without cerebellar CMBs but without other non-lobar CMBs, was seen in 8/13 (62%) with GRE CMBs and 19/30 with SWI CMBs (63%). Increased age and diabetes were associated with CMBs on both SWI and GRE. Conclusions: More than twice as many CMBs were detected on MRI SWI compared to GRE, with the same inter-rater reliability. SWI may be the preferred sequence for detecting CMBs, given its greater sensitivity. However, CMB distribution patterns and risk factors were similar regardless of sequence type, suggesting that the findings of studies using either sequence are comparable.
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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 | 0.007 | 0.026 |
| 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) | 0.002 | 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".