Simulation of the interaction of light and tissue in a large volume using a Markov chain Monte Carlo method
Bibliographic record
Abstract
Numerical simulation of the interaction between light and tissue is important for the design and analysis of many optical imaging modalities. Most current simulations are based on the Transport Theory of light in a dielectric, and only calculate the intensity of light in a volume. These simulations do not provide phase information, which is important for many biomedical imaging systems. We are interested in obtaining the optical field, magnitude and phase, due to the interaction of light with tissue. Therefore, we need to solve the integral equation for scalar scattering in a volume of interest. Since the wavelength of light is in the order of nanometres, simulation of volumes of more than a few millimetres requires intensive computational resources. For large volumes, Monte Carlo methods are a suitable choice because their computational complexity is independent of the mathematical dimension of the problem. Also by a careful selection of the random sampling scheme the number of samples needed can be further reduced. In this paper we present an implementation of a method to solve Fredholm integral equations of the second kind using Reversible Jump Markov chain Monte Carlo (RJMCMC). This method could be used to simulate light in tissue with very large electrical size, meaning tissue whose physical dimensions are much larger than the wavelength of light, by solving the integral equation of scalar scattering over a large domain. We implemented this method based on RJMCMC and present in this paper the results of applying it to solve integral equations of one and two dimensions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".