A transmissive laser speckle imaging technique for measuring deep tissue blood flow: An example application in finger joints
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Laser speckle perfusion imaging (LSPI) is a minimally invasive optical measure of relative changes in blood flow, providing real-time, high resolution, two-dimensional maps of vascular structure. Standard LSI imaging uses a light-reflective geometry that limits the measurement to a thin surface layer of 0.2-1 mm. The objective of this study was to test a new LSI instrument geometry with the laser source opposed to the image capture plane (light transmissive). Captured light then travels the entire tissue thickness (10-15 mm), sampling much deeper regions of interest than conventional optical imaging techniques. STUDY DESIGN: Reflective-light (conventional) and transmissive-light LSI modes were used to measure finger joint blood flow during a timed tourniquet occlusion of the brachial artery in volunteer participants. RESULTS: There was greatly increased visibility of vessels underlying the skin in the light-transmissive mode LSI mode. Established LSI algorithms were shown to still work in the light-transmissive mode, despite decorrelation due to finite laser coherence length and the light passing through a tissue thickness of 10-15 mm. CONCLUSION: Transmissive LSI can be used to measure blood flow deep (10-15 mm) into tissues. This could be useful for non-invasive measurements of finger joint synovial blood flow in diagnosing and treating peripheral vascular disorders, such as rheumatoid arthritis.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".