Characterization of Atherosclerotic Plaques by Laser Speckle Imaging
Bibliographic record
Abstract
BACKGROUND: A method capable of determining atherosclerotic plaque composition and measuring plaque viscoelasticity can provide valuable insight into intrinsic features associated with plaque rupture and can enable the identification of high-risk lesions. In this article, we describe a new optical technique, laser speckle imaging (LSI), that measures an index of plaque viscoelasticity. We evaluate the potential of LSI for characterizing atherosclerotic plaque. METHODS AND RESULTS: Time-varying helium-neon laser speckle images were acquired from 118 aortic plaque specimens from 14 human cadavers under static and deforming conditions (0 to 200 microm/s). Temporal fluctuations in the speckle patterns were quantified by exponential fitting of the normalized cross-correlation of sequential frames in each image series of speckle patterns to obtain the exponential decay time constant, tau. The decorrelation time constants of thin-cap fibroatheromas (TCFA) (tau=47.5+/-19.2 ms) were significantly lower than those of other atherosclerotic lesions (P<0.001), and the sensitivity and specificity of the LSI technique for identifying TCFAs were >90%. Speckle decorrelation time constants demonstrated strong correlation with histological measurements of plaque collagen (R=0.73, P<0.0001), fibrous cap thickness (R=0.87, P<0.0001), and necrotic core area (R=-0.81, P<0.0001). Under deforming conditions (10 to 200 microm/s), tau correlated well with cap thickness in necrotic core fibroatheromas (P>0.05). CONCLUSIONS: The measurement of speckle decorrelation time constant from laser speckle images provides an index of plaque viscoelasticity and facilitates the characterization of plaque type. Our results demonstrate that LSI is a highly sensitive technique for characterizing plaque and identifying thin-cap fibroatheromas.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".