Magnetic resonance imaging and three‐dimensional ultrasound of carotid atherosclerosis: Mapping regional differences
Bibliographic record
Abstract
PURPOSE: To evaluate differences in carotid atherosclerosis measured using magnetic resonance imaging (MRI) and three-dimensional ultrasound (3DUS). MATERIALS AND METHODS: Ten subject volunteers underwent carotid 3DUS and MRI (multislice black blood fast spin echo, T1-weighted contrast, double inversion recovery, 0.5 mm in-plane resolution, 2 mm slice, 3.0 T) within 1 hour. 3DUS and MR images were manually segmented by two observers providing vessel wall and lumen contours for quantification of vessel wall volume (VWV) and generation of carotid thickness maps. RESULTS: MRI VWV (1040 +/- 210 mm(3)) and 3DUS VWV (540 +/- 110 mm(3)) were significantly different (P < 0.0001). When normalized for the estimated adventitia volume, mean MRI VWV decreased 240 +/- 50 mm(3) and was significantly different from 3DUS VWV (P < 0.001). Two-dimensional carotid maps showed qualitative evidence of regional differences in the plaque and vessel wall thickness between MR and 3DUS in all subjects. Power Doppler US confirmed that heterogeneity in the common carotid artery in all patients resulted from apparent flow disturbances, not atherosclerotic plaque. CONCLUSION: MRI and 3DUS VWV were significantly different and carotid maps showed homogeneous thickness differences and heterogeneity in specific regions of interest identified as MR flow artifacts in the common carotid artery.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".