Ionization chamber‐based reference dosimetry of intensity modulated radiation beams
Bibliographic record
Abstract
The present paper addresses reference dose measurements using thimble ionization chambers for quality assurance in IMRT fields. In these radiation fields, detector fluence perturbation effects invalidate the application of open-field dosimetry protocol data for the derivation of absorbed dose to water from ionization chamber measurements. We define a correction factor C(Q)IMRT to correct the absorbed dose to water calibration coefficient N(D, w)Q for fluence perturbation effects in individual segments of an IMRT delivery and developed a calculation method to evaluate the factor. The method consists of precalculating, using accurate Monte Carlo techniques, ionization chamber, type-dependent cavity air dose, and in-phantom dose to water at the reference point for zero-width pencil beams as a function of position of the pencil beams impinging on the phantom surface. These precalculated kernels are convolved with the IMRT fluence distribution to arrive at the dose-to-water-dose-to-cavity air ratio [D(a)w (IMRT)] for IMRT fields and with a 10x10 cm2 open-field fluence to arrive at the same ratio D(a)w (Q) for the 10x10 cm2 reference field. The correction factor C(Q)IMRT is then calculated as the ratio of D(a)w (IMRT) and D(a)w (Q). The calculation method was experimentally validated and the magnitude of chamber correction factors in reference dose measurements in single static and dynamic IMRT fields was studied. The results show that, for thimble-type ionization chambers the correction factor in a single, realistic dynamic IMRT field can be of the order of 10% or more. We therefore propose that for accurate reference dosimetry of complete n-beam IMRT deliveries, ionization chamber fluence perturbation correction factors must explicitly be taken into account.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".