A fluence-convolution method to calculate radiation therapy dose distributions that incorporate random set-up error
Bibliographic record
Abstract
The International Commission on Radiation Units and Measurements Report 62 (ICRU 1999) introduced the concept of expanding the clinical target volume (CTV) to form the planning target volume by a two-step process. The first step is adding a clinically definable internal margin, which produces an internal target volume that accounts for the size, shape and position of the CTV in relation to anatomical reference points. The second is the use of a set-up margin (SM) that incorporates the uncertainties of patient beam positioning, i.e. systematic and random set-up errors. We propose to replace the random set-up error component of the SM by explicitly incorporating the random set-up error into the dose-calculation model by convolving the incident photon beam fluence with a Gaussian set-up error kernel. This fluence-convolution method was implemented into a Monte Carlo (MC) based treatment-planning system. Also implemented for comparison purposes was a dose-matrix-convolution algorithm similar to that described by Leong (1987 Phys. Med. Biol. 32 327-34). Fluence and dose-matrix-convolution agree in homogeneous media. However, for the heterogeneous phantom calculations, discrepancies of up to 5% in the dose profiles were observed with a 0.4 cm set-up error value. Fluence-convolution mimics reality more closely, as dose perturbations at interfaces are correctly predicted (Wang et al 1999 Med. Phys. 26 2626-34, Sauer 1995 Med. Phys. 22 1685-90). Fluence-convolution effectively decouples the treatment beams from the patient. and more closely resembles the reality of particle fluence distributions for many individual beam-patient set-ups. However, dose-matrix-convolution reduces the random statistical noise in MC calculations. Fluence-convolution can easily be applied to convolution/superposition based dose-calculation algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".