Incorporating geometric uncertainties into dose calculations with convolution: the effect of spatial invariance
Bibliographic record
Abstract
Convolution methods have been incorporated into dose calculations to model the effect of geometric uncertainties on the dose received. These methods assume spatial invariance of the dose distribution, although it is known that this is violated in practice. The magnitudes of the resulting errors are not well documented. The authors specifically address the issue of spatial invariance due to tissue inhomogeneities and surface contours. They accomplished this by comparing two approaches. First, the uncertainty in beam positioning was modeled with a Gaussian distribution. A static dose distribution (with surface and inhomogeneity corrections) was calculated and was convolved with the Gaussian to yield a "blurred" dose distribution incorporating the uncertainties. Second, the dose was calculated using a finite number of spatially displaced individual beams (each calculated with surface and inhomogeneity corrections) weighted by the same Gaussian for their displacement from the static beam position. The difference between the results of the two methods indicates the error in the convolution method. This analysis was performed for four phantoms with various surface curvature and internal inhomogeneities. Significant differences are observed due to the effect of surface curvature, while the errors due to internal inhomogeneities appear to be minor. It is concluded that for convolution algorithms to be of clinical use, the inaccuracy due to the effect of surface curvature needs to be addressed.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".