MR Imaging of the Arterial Vessel Wall: Molecular Imaging from Bench to Bedside
Bibliographic record
Abstract
Cardiovascular diseases remain the leading cause of morbidity and mortality in the Western world and developing countries. In clinical practice, in vivo characterization of atherosclerotic lesions causing myocardial infarction, ischemic stroke, and other complications remains challenging. Imaging methods, limited to the assessment luminal stenosis, are the current reference standard for the assessment of clinically significant coronary and carotid artery disease and the guidance of treatment. These techniques do not allow distinction between stable and potentially vulnerable atherosclerotic plaque. Magnetic resonance (MR) imaging is a modality well suited for visualization and characterization of the relatively thin arterial vessel wall, because it allows imaging with high spatial resolution and excellent soft-tissue contrast. In clinical practice, atherosclerotic plaque components of the carotid artery and aorta may be differentiated and characterized by using unenhanced vessel wall MR imaging. Additional information can be gained by using clinically approved nonspecific contrast agents. With the advent of targeted MR contrast agents, which enhance specific molecules or cells, pathologic processes can be visualized at a molecular level with high spatial resolution. In this article, the pathophysiologic changes of the arterial vessel wall underlying the development of atherosclerosis will be first reviewed. Then basic principles and properties of molecular MR imaging contrast agents will be introduced. Additionally, recent advances in preclinical molecular vessel wall imaging will be reviewed. Finally, the clinical feasibility of arterial vessel wall imaging at unenhanced and contrast material-enhanced MR imaging of the aortic, carotid, and coronary vessel wall will be discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".