Design of a Linear Wavenumber Spectrometer for Line Scanning Optical Coherence Tomography with 50 mm Focal Length Cylindrical Optics
Bibliographic record
Abstract
Optical coherence tomography (OCT) has a wide range of uses in bioimaging and nondestructive testing. Larger bandwidth light sources have recently been implemented to enhance measurement resolution. Increased bandwidth has a negative impact on spectral nonlinearity in k space, notably in the case of spectral domain OCT (SD-OCT). This nonlinearity reduces the depth-dependent signal sensitivity of the spectrometers. A grating and prism combination is extensively used for linearizing. In an earlier study, we used a combination of the reflective grating and prism, as well as a cylindrical mirror with a radius of 180 mm, to achieve a high SR ratio with low nonlinearity. A creative design for a spectrometer with a cylindrical mirror of radius 50 mm, a light source with a center wavelength of 830 ± 100 nm (μm−1 − 6.756 μm−1 in k-space), and a grating of 1600 lines/mm is presented in this work. The design optimization is performed using MATLAB and ZEMAX. In the proposed design, the nonlinearity error reduced from 157∘× μm to 10.75∘× μm within the wavenumber range considered. The sensitivity research revealed that, with the new design, the SR ratio is extremely sensitive to the imaging optics’ angles. To resolve this, a spectrometer based on Grism is introduced. We present a Grism-based spectrometer with an optimized SR ratio of 0.97 and nonlinearity of 0.792∘× μm (Δθ/Δk). According to the sensitivity study, the Grism-based spectrometer is more robust.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".