Hybrid intravascular imaging: recent advances, technical considerations, and current applications in the study of plaque pathophysiology
Bibliographic record
Abstract
Cumulative evidence from histology-based studies demonstrate that the currently available intravascular imaging techniques have fundamental limitations that do not allow complete and detailed evaluation of plaque morphology and pathobiology, limiting the ability to accurately identify high-risk plaques. To overcome these drawbacks, new efforts are developing for data fusion methodologies and the design of hybrid, dual-probe catheters to enable accurate assessment of plaque characteristics, and reliable identification of high-risk lesions. Today several dual-probe catheters have been introduced including combined near infrared spectroscopy-intravascular ultrasound (NIRS-IVUS), that is already commercially available, IVUS-optical coherence tomography (OCT), the OCT-NIRS, the OCT-near infrared fluorescence (NIRF) molecular imaging, IVUS-NIRF, IVUS intravascular photoacoustic imaging and combined fluorescence lifetime-IVUS imaging. These multimodal approaches appear able to overcome limitations of standalone imaging and provide comprehensive visualization of plaque composition and plaque biology. The aim of this review article is to summarize the advances in hybrid intravascular imaging, discuss the technical challenges that should be addressed in order to have a use in the clinical arena, and present the evidence from their first applications aiming to highlight their potential value in the study of atherosclerosis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".