Diffuse optical tomographic imaging of biological media by time-dependent parabolic SPN equations: a two-dimensional study
Bibliographic record
Abstract
We investigate the problem of retrieving the optical properties (absorption and scattering) of biological tissue from a set of optical measurements. A diffuse optical tomography (DOT) algorithm that incorporates constrained optimization methods is implemented. To improve image quality, the DOT algorithm exploits full time-domain data. The time-dependent parabolic simplified spherical harmonics equations (TD-pSPN) are used as the forward model. Time-dependent adjoint variables are resorted to in the calculation of the gradient of the objective function. Several numerical experiments for small geometric media with embedded inclusions that mimic small animal imaging are performed. In the experiments, optical coefficient values are varied in the range of realistic values for the near-infrared spectrum, including high absorption values. Single and multiparameter reconstructions are performed with the diffusion equation and higher orders of the TD-pSPN equations. The results suggest the DOT algorithm based on the TD-pSPN model outperforms the DE, and accurately reconstructs optical parameter distributions of biological media both spatially and quantitatively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".