Characterization of Atherosclerotic Plaques by Laser Speckle Imaging
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it