23 kHz MEMS based swept source for optical coherence tomography imaging
Bibliographic record
Abstract
The transition from benchtop to clinical system often requires the medical technology to be robust, portable and accurate. This poses a challenge to current swept source optical coherence tomography imaging systems, as the bulk of the systems footprint is due to laser components. With the recent advancement of micromachining technology, we demonstrate the characterization of a microelectromechanical system (MEMS) swept source laser for optical coherence tomography imaging (OCT). This laser utilizes a 2 degree of freedom MEMS scanning mirror and a diffraction grating, which are arranged in a Littrow configuration. This resulted in a swept source laser that was capable of scanning at 23.165 kHz (bidirectional) or 11.582 kHz (unidirectional). The free spectral range of the laser was ≈ 100 nm with a central wavelength of ≈ 1330 nm. The 6 dB roll off depth was measured to be at 2.5 mm. Furthermore, the structural morphology of a human finger and tadpole (Xenopus laevis) were evaluated. The overall volumetric footprint of the laser source was measured to be 70 times less than non-MEMS swept sources. Continued work on the miniaturization of OCT system is on going. It is hypothesized that the overall laser size can be reduced for suitable OCT imaging for a point of care application.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".