Advances in laser speckle imaging: From qualitative to quantitative hemodynamic assessment
Bibliographic record
Abstract
Laser speckle imaging (LSI) techniques have emerged as a promising method for visualizing functional blood vessels and tissue perfusion by analyzing the speckle patterns generated by coherent light interacting with living biological tissue. These patterns carry important biophysical tissue information including blood flow dynamics. The noninvasive, label-free, and wide-field attributes along with relatively simple instrumental schematics make it an appealing imaging modality in preclinical and clinical applications. The review outlines the fundamentals of speckle physics and the three categories of LSI techniques based on their degree of quantification: qualitative, semi-quantitative and quantitative. Qualitative LSI produces microvascular maps by capturing speckle contrast variations between blood vessels containing moving red blood cells and the surrounding static tissue. Semi-quantitative techniques provide a more accurate analysis of blood flow dynamics by accounting for the effect of static scattering on spatiotemporal parameters. Quantitative LSI such as optical speckle image velocimetry provides quantitative flow velocity measurements, which is inspired by the particle image velocimetry in fluid mechanics. Additionally, discussions regarding the prospects of future innovations in LSI techniques for optimizing the vascular flow quantification with associated clinical outlook are presented.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.003 | 0.002 |
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".