Light dosimetry for multiple cylindrical diffusing sources for use in photodynamic therapy
Bibliographic record
Abstract
Since prostatic carcinoma is usually multifocal within the prostate, effective photodynamic therapy (PDT) of prostatic carcinoma is expected to require the photochemical destruction of the entire organ. Accurate light dosimetry will be essential to avoid damage to proximal sensitive tissue such as the rectum. The prostate will be illuminated using interstitial cylindrical fibreoptic light sources and, because of the limited transparency of prostate tissue, these sources will be mounted in a parallel array analogous to the source array used in brachytherapy. Both source spacing and the light delivered to each source will control light dosimetry from a parallel array of fibreoptic sources implanted into tissue. Clinical PDT will require dose planning in order to determine the position and illumination of each source prior to treatment, but unfortunately few methods of predicting light fluence from cylindrical interstitial sources currently exist. In this paper, a novel light fluence model is used to predict tissue transillumination resulting from cylindrical interstitial sources. The cylindrical source is modelled as a finite array of infinitesimal small sources using Christian Huygens' famous single-slit diffraction model. We show that this source model when combined with a robust derivation of fluence in a spherical geometry using diffusion theory, accurately predicts fluence levels from a single cylindrical source in a variety of media. This method is found to retain its accuracy near the sources. With a simple extension, this fluence model is used to predict the light fluence levels from an array of three sources and the predicted fluence is found to compare favourably with experimental data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".