Po‐Poster ‐ 33: A finite element model for bioluminescence imaging in small animals
Bibliographic record
Abstract
Bioluminescence imaging is a powerful technique for visualizing gene expression in small animals but it suffers a serious limitation: the absorption and scattering of light in tissue. Several factors influence the image: source strength and depth, effective numerical aperture of the imaging optics, and attenuation by the tissue between the source and the camera. Our overall goal is to account for these effects and to recover the actual strength and spatial location of the bioluminescence sources in vivo. An essential first step in this research is to develop a physical model that accurately predicts the light reaching the surface of the animal for an arbitrary distribution of sources and optical absorption and scattering coefficients. The calculations must be fast, so that the model can be used eventually in an iterative algorithm to solve the inverse problem. We use the diffusion approximation, valid when scattering dominates absorption and when it is not necessary to calculate the light field close to sources. The diffusion equation expresses the light fluence rate as a function of position and the spatially dependent absorption coefficient, scattering coefficient, and source function. A finite element code called NIRFAST has been developed to generate numerical solutions. NIRFAST has been implemented in MATLAB and uses a 2 or 3 dimensional model to represent the object. The absorption and scattering coefficients are specified at each node of the mesh. We have assigned “reasonable” values of the absorption and scattering coefficients to each node based on tissue identification by x‐ray CT.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".