A Laser Speckle Imaging Technique for Measuring Tissue Perfusion
Bibliographic record
Abstract
Laser Doppler imaging (LDI) has become a standard method for optical measurement of tissue perfusion, but is limited by low resolution and long measurement times. We have developed an analysis technique based on a laser speckle imaging method that generates rapid, high-resolution perfusion images. We have called it laser speckle perfusion imaging (LSPI). This paper investigates LSPI output and compares it to LDI using blood flow models designed to simulate human skin at various levels of pigmentation. Results show that LSPI parameters can be chosen such that the instrumentation exhibits a similar response to changes in red blood cell concentration (0.1%-5%, 200 microL/min) and velocity (0-800 microL/min, 1% concentration) and, given its higher resolution and quicker response time, could provide a significant advantage over LDI for some applications. Differences were observed in the LDI and LSPI response to tissue optical properties. LDI perfusion values increased with increasing tissue absorption, while LSPI perfusion values showed a slight decrease. This dependence is predictable, owing to the perfusion algorithms specific to each instrument, and, if properly compensated for, should not influence each instrument's ability to measure relative changes in tissue perfusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".