Sci—Fri PM: Delivery — 08: Characterizing the spatially varying fluence and spectra of a kV imaging source for dose calculations
Bibliographic record
Abstract
Kilovoltage (kV) daily image‐guided radiotherapy (IGRT) procedures accumulate radiation dose within the patient that is currently not routinely incorporated in the treatment plan. As part of the process of developing a patient‐specific kV dose computation tool, the kV x‐ray source must be characterized. We propose a simple, clinically feasible experimental characterization method using in‐air dose measurements along the transverse axis. We determine half‐value layer (HVL) along the transverse axis, from which we derive the HVL‐specific mass‐absorption coefficient, which is used to determine beam fluence. These values are interpolated over the entire field. The spectrum at each interpolation point in the field is found from HVL and accelerating potential (kVp) using third‐party software Spektr. We use this method to characterize the spatially varying fluence and spectra of a Varian® On‐Board Imaging® source for energies 80, 100 and 125 kVp. This characterization is used to compute dose within a heterogeneous phantom, using our previously validated in‐house dose computation software, which we compare with relative dose measurements. We show that for a 10×10 cm2 field size using no added filtration, the agreement for all three energies is within 2% for the central depth‐dose profile and within 2.6% for the transverse profiles. This clinically feasible experimental characterization method for kV imaging sources represents a crucial step in the development of a patient‐specific dose computation tool.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.286 | 0.139 |
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".