SU‐E‐I‐70: Characterisation of the Parameters Defining a Kilovoltage Source for Accurate Dose Computation
Bibliographic record
Abstract
Purpose: To perform a sensitivity analysis of the parameters that define beam quality for a kilovoltage (kV) x‐ray sourceMethods: Using a previously developed hybrid approach to calculate radiation dose deposited by x‐ray beams of energies <150 kVp, we computed dose inside a simulated heterogeneous phantom for varying beam spectra to evaluate the sensitivity of calculated dose to half‐value layer (HVL). The approach involves computing the primary photon component deterministically and scattered component stochastically, accounting for the real micro cross sections of the materials involved. We characterized the spectrum of the Varian® On‐Board Imaging® units at our institution by HVL measurements made using of a Farmer‐type Capintec ion chamber (0.06cc) in air. We compared doses computed with our characterized source spectrum with measurements inside water‐equivalent Gammex® Solid Water® phantom. Measurements were done using a 10×10 cm2 field at 100 cm SSD at every centimeter for depths 1 to 12 cm. Results: We found that measuring kV and HVL gives us sufficient beam quality information for accurate [>0.5%] x‐ray dose calculations. We also showed that HVL varies by less than 0.1 mm Al for field sizes over 5×5 cm2, allowing us to use one HVL irrespective of field size. Agreement between calculations and experimental measurements was better than 2% for a transverse profile and the central axis depth dose. This implies that our approach to characterizing the beam quality of a kV source is suitable for ensuring accurate results Conclusions: We have determined the parameters we need to characterize the source in order to obtain good agreement between theoretical calculations and experimental measurements. This is a crucial step in developing an independent tool to calculate patient dose from kV beams such as cone‐beam CT and brings us closer to our goal of calculating patient‐specific dose from imaging procedures.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".