Robust estimation of vessel misfocus and real-time misfocus correction in laser speckle contrast imaging
Bibliographic record
Abstract
Laser Speckle Contrast Imaging (LSCI) is a flexible, non-invasive, label-free technique to measure relative blood flow speeds in-vivo. Near IR illumination allows deep tissue penetration due to low tissue absorption in that wavelength range. However, the low absorption leads to a reduced observed image contrast between tissue and blood vessels. This leads to a challenge in determining and automatically adjusting the best focus location invivo. Traditional autofocus algorithms that are based on either intensity contrast or frequency domain analysis do not work well during flow imaging with the LSCI technique, due to increased speckle and low contrast in the image. Using the LSCI-derived contrast ratio K directly, over a vessel of interest, provides a better metric for determining the location of imaging system focal plane, but the method is not robust as it is possesses low signal-to-noise ratio (SNR) within a single frame. In this work we use a different metric, kurtosis of the flow profile cross-section, to estimate the degree of misfocus (axial deviation of imaging system focal plane from the imaged blood vessel) and provide a feedback mechanism for robust autofocusing during blood flow imaging in a rats brain. We demonstrate via flow imaging simulations, imaging of flow in microfluidic capillaries, and in-vivo imaging of blood flow in brains of anaesthetized rats that this metric allows for the determination of the location of best focus and assessing the degree of misfocus.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".