Correcting spatial-spectral crosstalk and chromatic aberrations in broadband line-scan spectral-domain OCT images
Bibliographic record
Abstract
Digital correction of optical aberrations allows for high-resolution imaging across the full depth range in optical coherence tomography (OCT). Many digital aberration correction (DAC) methods have been proposed in the past to evaluate and correct monochromatic error in OCT images. However, other factors that deteriorate the image quality have not been fully investigated. Specifically, in a broadband line-scan spectral-domain OCT system (LS-SD-OCT), photons with different wavelengths scattered from the same transverse location and in the imaged object will be projected onto different spatial coordinates onto the 2D camera sensor, which in this work is defined as spatial-spectral crosstalk. In addition, chromatic aberrations in both axial and lateral directions are not negligible for broad spectral bandwidths. Here we present a novel approach to digital recovery of the spatial resolution in images acquired with a broadband LS-SD-OCT, which addresses these two main factors that limit the effectiveness of DAC for restoring diffraction-limited resolution in LS-SD-OCT images. In the proposed approach, spatial-spectral crosstalk and chromatic aberrations are suppressed by the registration of monochromatic sub-band tomograms that are digitally corrected for aberrations. The new method was validated by imaging a standard resolution target, a microspheres phantom, and different biological tissues. LS-SD-OCT technology combined with the proposed novel image reconstruction method could be a valuable research tool for various biomedical and clinical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".