Three-dimensional light-tissue interaction models for bioluminescence tomography
Bibliographic record
Abstract
Many diagnostic and therapeutic approaches in medical physics today take advantage of the unique properties of light and its interaction with tissues. Because light scatters in tissue, our ability to develop these techniques depends critically on our knowledge of the distribution of light in tissue. Solutions to the diffusion equation can provide such information, but often lack the flexibility required for more general problems that involve, for instance, inhomogeneous optical properties, light polarization, arbitrary three-dimensional geometries, or arbitrary scattering. Monte Carlo techniques, which statistically sample the light distribution in tissue, offer a better alternative to analytical models. First, we discuss our implementation of a validated three-dimensional polarization-sensitive Monte Carlo algorithm and demonstrate its generality with respect to the geometry and scattering models it can treat. Second, we apply our model to bioluminescence tomography. After appropriate genetic modifications to cell lines, bioluminescence can be used as an indicator of cell activity, and is often used to study tumour growth and treatment in animal models. However, the amount of light escaping the animal is strongly dependent on the position and size of the tumour. Using forward models and structural data from magnetic resonance imaging, we show how the models can help to determine the location and size of tumour made of bioluminescent cancer cells in the brain of a mouse.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".