Photoacoustic cardiovascular imaging: a new technique for imaging of atherosclerosis and vulnerable plaque detection
Bibliographic record
Abstract
Abstract The sudden rupture of an atherosclerotic plaque is one of the main causes of stroke and stroke induced death. Plaque composition plays a critical role in plaque rupture. In order to differentiate between different plaque components, an imaging technique suitable for patient follow-up is needed. Photoacoustic (PA) imaging (PAI), a relatively new imaging technique, can also be used for cardiovascular imaging as it resolves optical contrast with ultrasonic resolution, visualizes oxygenated and deoxygenated haemoglobin and a large range of optical agents. PAI can be used to visualize molecular tissue changes, not only stenosis, and relates to the evaluation treatment protocols and/or post-procedural follow-up. In this review, we explain the principles of PAI, describe the recent developments in PAI systems used for non-invasive carotid imaging and intravascular imaging of coronary atherosclerosis and suggest areas of future research that will help bring PAI imaging towards clinical cardiovascular imaging.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".