Three-dimensional diffuse optical tomography with full multi-view time-domain data
Bibliographic record
Abstract
In this work, synthetic time-domain data are generated as if it were collected with a state-of-the-art multi-view experimental optical scanner developed in our group for small animal imaging, and used in a tomographic image reconstruction algorithm. The collected data comprises full time-dependent optical signals leaving the biological medium and acquired all around the medium. The diffuse optical tomography (DOT) algorithm relies on the time dependent parabolic simplified spherical harmonics (TD-pSPN) equations as the forward model to recover the 3D absorption and diffusion coefficient maps of the medium. The inverse problem is casted and solved as an iterative constrained optimization problem where an objective function determines the accuracy of the forward model predictions at each iteration. Time-dependent adjoint variables are introduced to accelerate the calculation of the gradient of the objective function. A three-dimensional case involving an absorption heterogeneity in a homogeneous medium is presented, reproducing practical situations encountered in our lab. The results support our hypothesis that accurate quantitative 3D maps of optical properties of biological tissues can be retrieved using intrinsic measurements obtained with our experimental scanner along with our DOT algorithm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".