Interpolation and extrapolation of dose measurements with different detector sizes to improve the spatial resolution of radiotherapy dosimetry as demonstrated for helical tomotherapy
Bibliographic record
Abstract
A new technique for intensity modulated radiation therapy (IMRT) delivery is helical tomotherapy (HT). Like most IMRT delivery methods, HT utilizes many small fields as part of the treatment plan, which can be difficult to characterize. A novel technique for small field characterization, based on inter- and extrapolation of ion chamber readings, is presented in the context of HT. As a fan beam is characterized by its thickness and output factor, plane parallel chambers with different active volumes were used to scan the fan beam profiles. The fan beam thickness (FBT) can be determined from the thickness measured with the chamber by extrapolating to an infinitesimally small chamber size. The effective output was derived from the integral under the dose profile divided by the FBT. This was done for five FBTs and demonstrated a sharp fall off in dose when the FBT decreased below 8 mm. Similar techniques can be applied to other IMRT techniques to improve the characterization of various beam parameters.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".