Interference-free Detection of Lipid-laden Atherosclerotic Plaques by 3D Co-registration of Frequency-Domain Differential Photoacoustic and Ultrasound Radar Imaging
Bibliographic record
Abstract
As lipid composition of atherosclerotic plaques is considered to be one of the primary indicators for plaque vulnerability, a diagnostic modality that can sensitively evaluate their necrotic core is highly desirable in atherosclerosis imaging. In this regard, intravascular photoacoustic (IVPA) imaging is an emerging plaque detection modality that provides lipid-specific chemical information of arterial walls. Within the near-infrared window, a 1210-nm optical source is usually chosen for IVPA applications because lipid exhibits a strong absorption peak at that wavelength. However, other arterial tissues also show some degree of absorption near 1210 nm and generate undesirable interfering PA signals. In this study, a novel wavelength-modulated Intravascular Differential Photoacoustic Radar (IV-DPAR) modality was introduced as an interference-free detection technique for a more accurate and reliable diagnosis of plaque progression. By using two low-power continuous-wave laser diodes in a differential manner, IV-DPAR could efficiently suppress undesirable absorptions and system noise, while dramatically improving system sensitivity and specificity to cholesterol, the primary ingredient of plaque necrotic core. When co-registered with intravascular ultrasound imaging, IV-DPAR could sensitively locate and characterize the lipid contents of plaques in human atherosclerotic arteries, regardless of their size and depth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".