Interferometric speckle contrast optical spectroscopy (iSCOS) in continuous-wave parallel interferometric near-infrared spectroscopy (CW-πNIRS)
Bibliographic record
Abstract
Optical methods enable continuous, noninvasive cerebral blood flow (CBF) monitoring. Diffuse correlation spectroscopy (DCS) estimates CBF through temporal correlation analysis of scattered light but is limited by low detection throughput. Parallelizing DCS enhances performance but requires costly ultra-fast (∼1 MHz) detectors, complicating continuous measurements. An alternative approach analyzes spatial correlations using speckle contrast, inversely proportional to blood flow, captured with slower two-dimensional sensors. In this study, we present continuous-wave parallel interferometric near-infrared spectroscopy (CW-πNIRS), employing interferometry combined with a high-speed 2D camera, as a novel method uniquely suited for spatial correlation measurements. By leveraging interferometric detection, our approach provides a synthetic multi-exposure capability for direct quantitative comparisons between spatial (speckle contrast) and temporal (autocorrelation) methods for CBF monitoring. Numerical simulations, incorporating interferometric reference fields, and tissue-mimicking phantom validations demonstrated robust, and stable speckle contrast estimates. Finally, in vivo experiments confirmed the method’s potential for effective human cerebral blood flow monitoring, highlighting practical advantages and providing a clear pathway towards clinical implementation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".