Two-dimensional velocity estimation for Doppler optical coherence tomography
Bibliographic record
Abstract
In this paper we demonstrate a new algorithm for use in Doppler Optical Coherence Tomography (DOCT) to allow the detection of flow with a peak velocity of over 1.5 m/s. Previous Doppler estimation methods have utilized a transverse Kasai (TK) autocorrelation technique which computes the phase difference between points adjacent in time at the same spatial location, hereon referred to as transversely adjacent points. The maximum detectable TK velocity is low due to the small axial scanning frequency, fa which creates aliasing. To overcome the low sampling rate, we propose using data acquired in the axial direction which has a sampling rate orders of magnitude larger. Taking an autocorrelation in the depth, or axial direction, yields a quantity that can be related to the mean backscattered frequency. We demonstrate that through subtraction of the axial autocorrelation of a moving scatterer from that of a stationary scatterer at the same spatial location, one is able to obtain the Doppler shift with a much higher non-aliased limit. We have defined this method the axial Kasai (AK) technique. Through use of the AK, we demonstrate maximum non-aliased Dopler frequency estimate on a time domain DOCT system to be increased from the TK limit of ±4 kHz to the AK limit of ±1.6 MHz. In contrast to the high detection range of the AK, the TK maintains superior velocity resolution for low flow rates. Through a combined approach with the AK we have demonstrated a dynamic frequency range of over 100 dB with a velocity detection range from 10 &mgr;m/s to over 1.5 m/s. The velocity range has been extended to span both microcirculation and cardiac blood velocities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".