Complex swept source optical coherence tomography with ultra-small fiber probe for biomedical imaging
Bibliographic record
Abstract
We demonstrate a catheter-based complex swept-source optical coherence tomography (SS-OCT) system using a 3x3 Mach-Zehnder quadrature interferometer and an ultra-small optic probe. Design and fabrication of fiber lens for ultrasmall optic probes are presented first. We compare in detail measured performance with expected theoretical performance. Then, we present a 3x3 Mach-Zehnder quadrature interferometer to acquire a complex interferometric signal for SS-OCT. We introduce an unbalanced differential detection method to improve the overall utilization of optical power and provide simultaneous access to the complementary phase components of the complex interferometric signal. No calculations by trigonometric relationships are needed. We compare the performance for our setup to that of a similar interferometer with a commonly used balanced detection technique. We demonstrate complex conjugate artifact suppression of 27 dB obtained in our swept-source optical coherence tomography using our unbalanced differential detection. OCT in vivo and ex vivo images shown in this presentation indicate that our catheter-based complex SS-OCT system is capable for imaging of biomedical tissues and inside organs for human and animals.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".