SU‐E‐T‐796: Variation of Surface Photon Energy Spectra On Bone Heterogeneity and Beam Obliquity Between Flattened and Unflattened Beam
Bibliographic record
Abstract
Purpose: This study investigates the spectra of surface photon energy and energy fluence in the bone heterogeneity and beam obliquity using flattened and unflattened photon beams. The spectra were calculated in a bone and water phantom using Monte Carlo simulation (the EGSnrc code). Methods: Spectra of energy, energy fluence and mean energy of the 6 MV flattened and unflattened photon beams (field size = 10 × 10 cm2) produced by a Varian TrueBEAM linear accelerator were calculated at the surfaces of a bone and water phantom using Monte Carlo simulations. The spectral calculations were repeated with the beam angles turned from 0° to 15°, 30° and 45° in the phantoms. Results: It is found that the unflattened photon beams contained more photons in the low‐energy range of 0 – 2 MeV than the flattened beams with a flattening filter. Compared to the water phantom, both the flattened and unflattened beams had slightly less photons in the energy range < 0.4 MeV when a bone layer of 1 cm is present under the phantom surface. This shows that the presence of the bone decreased the low‐energy photons backscattered to the phantom surface. When the photon beams were rotated from 0° to 45°, the number of photon and mean photon energy increased with the beam angle. This is because both the flattened and unflattened beams became more hardened when the beam angle increased. With the bone heterogeneity, the mean energies of both photon beams increased correspondingly. This is due to the absorption of low‐energy photons by the bone, resulting in more significant beam hardening. Conclusion: The photon spectral information is important in studies on the patient's surface dose enhancement when using unflattened photon beams in radiotherapy.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".