A surface energy spectral study on the bone heterogeneity and beam obliquity using the flattened and unflattened photon beams
Bibliographic record
Abstract
AIM: Using flattened and unflattened photon beams, this study investigated the spectral variations of surface photon energy and energy fluence in the bone heterogeneity and beam obliquity. BACKGROUND: Surface dose enhancement is a dosimetric concern when using unflattened photon beam in radiotherapy. It is because the unflattened photon beam contains more low-energy photons which are removed by the flattening filter of the flattened photon beam. MATERIALS AND METHODS: We used a water and bone heterogeneity phantom to study the distributions of energy, energy fluence and mean energy of the 6 MV flattened and unflattened photon beams (field size = 10 cm × 10 cm) produced by a Varian TrueBEAM linear accelerator. These elements were calculated at the phantom surfaces using Monte Carlo simulations. The photon energy and energy fluence calculations were repeated with the beam angle turned from 0° to 15°, 30° and 45° in the water and bone phantom. RESULTS: Spectral results at the phantom surfaces showed that the unflattened photon beams contained more photons concentrated mainly in the low-energy range (0-2 MeV) than the flattened beams associated with a flattening filter. With a bone layer of 1 cm under the phantom surface and within the build-up region of the 6 MV photon beam, it is found that both the flattened and unflattened beams had slightly less photons in the energy range <0.4 MeV compared to the water phantom. This shows that the presence of the bone decreased the low-energy photon backscatters to the phantom surface. When both the flattened and unflattened photon beams were rotated from 0° to 45°, the number of photon and mean photon energy increased. This indicates that both photon beams became more hardened or penetrate when the beam angle increased. In the presence of bone, the mean energies of both photon beams increased. This is due to the absorption of low-energy photons by the bone, resulting in more beam hardening. CONCLUSIONS: This study explores the spectral relationships of surface photon energy and energy fluence with bone heterogeneity and beam obliquity for the flattened and unflattened photon beams. The photon spectral information is important in studies on the patient's surface dose enhancement 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".