Scan–Rescan reproducibility of carotid bifurcation geometry from routine contrast‐enhanced MR angiography
Bibliographic record
Abstract
PURPOSE: To demonstrate the feasibility of rapid and reliable geometric characterization of normal carotid bifurcation geometry from routine 3D contrast-enhanced magnetic resonance (MR) angiograms. MATERIALS AND METHODS: Repeat scans of 61 participants, acquired as part of the Atherosclerosis Risk in Communities (ARIC) Carotid MRI substudy, were digitally segmented using automated 3D level set methods, relying on an operator only to select the branch endpoints and thresholds for the 3D lumen surface initialization. Geometric factors characterizing the 3D lumen geometry were then extracted automatically. RESULTS: Of 122 scans, 117 could be segmented within 5 minutes each, with 40% being of sufficiently high quality to require less than 2 minutes each. Irrespective of scan quality, geometric factors were found to be highly reproducible, with intraclass correlation coefficients (ICCs) typically above 0.9. The reconstructed lumen surfaces were reproducible to <0.3 mm on average, comparable to previous MRI-based reproducibility studies. Owing to the automated nature of the analysis, operator reliability was near-perfect (ICC >0.99), with lumen surface differences <0.1 mm. CONCLUSION: The 3D geometry of the carotid bifurcation can be characterized rapidly and with a high degree of consistency, even for suboptimal image qualities. This bodes well for large-scale retrospective or prospective studies aimed at teasing out the influence of local vs. systemic risk factors for early atherosclerosis.
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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 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".