Combined acoustic-photoacoustic and fluorescence imaging catheter for the detection of the atherosclerotic plaque
Bibliographic record
Abstract
In industrialized countries, cardiovascular diseases remain the main cause of mortality. The detection of atherosclerosis and its associated plaque using imaging techniques allows studying the efficacy of new drugs in vivo. Intravascular ultrasound (IVUS) imaging has been demonstrated to be a powerful tool to uncover structural information of atherosclerotic plaques. Recently, intravascular photoacoustic (IVPA) has been combined with IVUS imaging to add functional and/or molecular information. The IVPA/IVUS combination has been demonstrated in phantoms and ex vivo tissues to provide relevant information about the composition of the plaque, as well as its vulnerability. In this work, we extend previous work by developing a combined IVPA/IVUS system using a rotating ultrasound transducer in a catheter to which an optical fiber is attached. In addition, a third modality was included through fluorescence detection in the same fiber at a distinct wavelength from PA, opening the door to complementary information using fluorescence activatable probes. Cylindrical silicon phantoms with inclusions containing fluorophores or ink were used to validate the system. Bleaching of the fluorophore by the pulsed laser used for photoacoustic was quantified. IVUS images were obtained continuously and used to co-register photoacoustic and fluorescence signals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".