Collision-kerma conversion between dose-to-tissue and dose-to-water by photon energy-fluence corrections in low-energy brachytherapy
Bibliographic record
Abstract
The AAPM TG-43 brachytherapy dosimetry formalism, introduced in 1995, has become a standard for brachytherapy dosimetry worldwide; it implicitly assumes that charged-particle equilibrium (CPE) exists for the determination of absorbed dose to water at different locations, except in the vicinity of the source capsule. Subsequent dosimetry developments, based on Monte Carlo calculations or analytical solutions of transport equations, do not rely on the CPE assumption and determine directly the dose to different tissues. At the time of relating dose to tissue and dose to water, or vice versa, it is usually assumed that the photon fluence in water and in tissues are practically identical, so that the absorbed dose in the two media can be related by their ratio of mass energy-absorption coefficients. In this work, an efficient way to correlate absorbed dose to water and absorbed dose to tissue in brachytherapy calculations at clinically relevant distances for low-energy photon emitting seeds is proposed. A correction is introduced that is based on the ratio of the water-to-tissue photon energy-fluences. State-of-the art Monte Carlo calculations are used to score photon fluence differential in energy in water and in various human tissues (muscle, adipose and bone), which in all cases include a realistic modelling of low-energy brachytherapy sources in order to benchmark the formalism proposed. The energy-fluence based corrections given in this work are able to correlate absorbed dose to tissue and absorbed dose to water with an accuracy better than 0.5% in the most critical cases (e.g. bone tissue).
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".