On the overestimation of early wall thickening at the carotid bulb by black blood MRI, with implications for coronary and vulnerable plaque imaging
Bibliographic record
Abstract
Black blood MRI is an attractive tool for monitoring normal and pathological wall thickening; however, limited spatial resolutions can conspire with complex vascular geometries to distort the appearance of the wall in ways hitherto unclear. To elucidate this, a thin-walled cylinder model was developed to predict the composite effects of obliqueness, in-plane resolution and voxel anisotropy on the accuracy of MRI-derived wall thickness measurements. These predictions were validated by means of imaging of a thin-walled carotid bifurcation phantom. Typical thick-slice axial acquisitions were found to result in artifactual wall thickening at the carotid bulb, owing to its obliqueness to the nominal imaging plane. Obliqueness was less problematic for near-isotropic resolutions; however, the obligatory reduction of in-plane resolution served to inflate wall thicknesses uniformly by up to 50%. Moreover, the nonlinear relationship between wall thickness and its overestimation served to mask genuine differences in wall thickness, an effect predicted to be worse for thinner coronary artery walls and plaque caps. Therefore, care must be taken when interpreting black blood MRI wall thickness measurements in the presence-or absence-of observed differences within or between individuals.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 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".