Atherosclerotic Plaque Characterization by MR Imaging
Bibliographic record
Abstract
The MR imaging of carotid artery and aortic plaque has undergone significant improvement in the last decade. Early studies utilizing ex vivo specimens and spin echo or fast spin echo imaging, led to the conclusion that T2-weighting was the best single contrast to characterize carotid plaque morphology. On these images, the fibrous plaque appears bright and the lipid core is dark; thrombus can have variable intensity. There can be an overlap in T2w signal intensities among the various plaque components, which can be partially offset by the use of qualitative or multi-spectral analysis of multiple contrast images. With improvements in coil design, sequence design, main field and gradient capabilities, accurate in vivo differentiation and measurement of these various plaque components should be possible in a few years. Carotid and aortic plaque burden can be accurately measured in vivo today; ongoing longitudinal studies should lead to a better understanding of the relationship between plaque burden and the risk of thromboembolic complications, as well as the effect of diet and drug therapy in hyperlipidemic patients. With these developments in place or soon to be available, MR imaging of the diseased carotid artery and aortic wall may prove to be even more important than MR angiography or other current clinical tests.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".