Enhancement of optical penetration depth of LED-based NIRS systems by comparing different beam profiles
Bibliographic record
Abstract
Abstract Near-Infrared Spectroscopy (NIRS) is a non-invasive brain imaging technique involving the quantification of oxy and deoxy-hemoglobin concentrations resolved from the measurement of Near-Infrared (NIR) light attenuation within the tissue. Previous studies have shown that NIR light is more influenced by the optical properties of the superficial layers than those of the deeper target layers such as cortex. NIR light produced by the Laser source penetrates deeper regions of the tissue rather than the LED source although Laser needs more expensive instrumentation. In this study, we investigate the effect of Uniform and Gaussian beam profiles on the enhancement of LED light penetration depth. The latter beam profiles were generated and compared using Flat and Aspherical lenses applied to the LED sources. In order to increase the signal to noise ratio, the lenses were also applied to the light detector. For performance analysis, two experiments were carried out by scanning the intra space of a liquid phantom by static and dynamic (pulsating) absorbers. Monte Carlo simulations were also carried out to be compared with the experiment. The results showed that Gaussian beam profile and in particular, Bi-Convex lenses applied to both source and detector leads to a greater light penetration depth in the liquid phantom close to that of a Laser source.
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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) | 0.001 | 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) | 0.001 | 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".