Development of a scatter correction technique and its application to HDR I192r multicatheter breast brachytherapy
Bibliographic record
Abstract
This article introduces a scatter correction (SC) technique for high-dose-rate (HDR) 192Ir brachytherapy dose calculations in the absence of a full scatter environment near the skin. The technique uses dosimetry data derived by Monte Carlo (MC) simulations for the Nucletron microSelectron v2 HDR 192Ir source. The data include the primary and scatter components of the radial dose function and the anisotropy function in addition to a SC table. The dose to a point of interest for each dwell position is estimated by first calculating the primary and scatter doses in an infinite water phantom. The scatter dose is then scaled by a SC factor that depends on the distances between the point of interest, the dwell positions, and the body contour of the patient. SC calculations in water phantoms of three different shapes, as well as computed tomography-based geometries of 18 multicatheter breast patients, are compared with Task Group 43 (TG-43) and PTRAN_CT MC calculations. The SC calculations show improvement over TG-43 for all test cases while taking 50% longer to run. The target and skin doses for the breast patient plans are unaffected by tissue inhomogeneities, as indicated by an agreement better than 1% between the SC and MC results. On average, TG-43 overestimates the target coverage by 2% and the dose to the hottest 0.1 cm3 (D0.1 cc) of the skin by 5%. The low-density lung causes the lung and heart D0.1 cc to differ by up to 3% for the SC method and by 2%-5% for TG-43 compared with MC calculations. The SC technique is suitable for HDR 192Ir dose calculations near the skin provided that the dose is nearly unperturbed by internal inhomogeneities. It has been validated for multicatheter breast brachytherapy.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".