Scan–rescan and intra-observer variability of magnetic resonance imaging of carotid atherosclerosis at 1.5 T and 3.0 T
Bibliographic record
Abstract
Carotid atherosclerosis measurements for eight subjects at baseline and 14 +/- 2 days later were examined using 1.5 T and 3.0 T magnetic resonance imaging (MRI). A single observer blinded to field strength, subject and timepoint manually segmented carotid artery wall and lumen boundaries in randomized images in five measurement trials. Mean increases in the signal-to-noise ratios (SNR) for T1-weighted images acquired at 3.0 T compared to 1.5 T were 90% (scan) and 80% (rescan). Despite significantly improved SNR and contrast-to-noise ratios (CNR) for images acquired at 3.0 T, vessel wall volume (VWV) intra-observer variability was not significantly different using coefficients of variation (COV), and intraclass correlation coefficients (ICC). VWV interscan variability and consistency at both field strengths were not statistically different (1.5 T/3.0 T COV = 5.7%/7.8%, R(2) = 0.96 for 1.5 T and R(2) = 0.87 for 3.0 T). A two-way analysis of variance showed a VWV dependence on field strength but not scan timepoint. In addition, a paired t-test showed significant differences in VWV measured at 3.0 T as compared to 1.5 T. These results suggest that although images acquired at 1.5 T have lower SNR and CNR VWV, measurement variability was not significantly different from 3.0 T VWV and that VWV is field-strength dependent which may be an important consideration for longitudinal studies.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".