Diffuse optical tomography of absorption in biological media using time-dependent parabolic simplified spherical polynomials equations
Bibliographic record
Abstract
We present a diffuse optical tomography (DOT) algorithm for imaging the absorption distribution in a biological tissue using time-domain optical measurements. The time-dependent parabolic simplified spherical polynomials approximation of the radiative transfer equation (the TD-pSP<sub>N</sub> model) serves as the forward model. The DOT algorithm is implemented using a nested analysis and design (NAND) method developed for solving constrained optimization problems. Numerical experiments are provided for small geometry media to mimic small animal imaging. In these experiments, the optical absorption coefficient value is varied within typical values found in the near infrared range for biological tissues, including high absorption values. The results show good spatial and quantitative reconstructions and support our TD-pSP<sub>N</sub>-based DOT algorithm as an accurate approach to image absorption in biological media.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".