A new deconvolution technique for time-domain signals in diffuse optical tomography without a priori information
Bibliographic record
Abstract
The present work will serve in a diffuse optical tomography (DOT) scanner that we are developing for small animal non-contact molecular imaging. We present a new method for deconvoluting time-domain signals for use in DOT. Time-domain signals represents reemitted light intensity as a function of time when the medium is excited by ultra-short laser pulses. Actually, each signal equals the convolution between the light propagation in the medium and the impulse response of the detection system, so-called the instrument response function (IRF). Moreover, Poisson noise present in the system has to be considered. Time-domain signals directly depend on the optical properties of a medium and so contain additional information (compared to continuous-wave signals) that should be exploited in reconstruction algorithms. As an advantage, our deconvolution method does not use a priori information about the signal. It is important to remove the IRF and noise from measured signals in order to keep only the true signal, which has a direct link to medium properties.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".