Poster — Thur Eve — 17: Effect of Heterogeneities Due to Kilo‐Voltage Photon Beam Energy in Small‐Animal Irradiation: A Monte Carlo Evaluation
Bibliographic record
Abstract
This study evaluated the effect of tissue heterogeneities due to photon beams in the kilo‐voltage energy range in small‐animal irradiation. Monte Carlo (MC) simulation (EGSnrc code) was used. Three MC mouse phantoms were generated from a single mouse CT image set. These phantoms were generated by overriding the relative electron density of no voxels (heterogeneous), all voxels (homogeneous) and the bone voxels (bone homogeneous) to one. Phase‐space files of the 100 and 225 kVp photon beams produced by a small animal irradiator were generated using BEAMnrc. A 360 deg photon arc was simulated for treatment of the lung, and 3D dose calculations were carried out for the three phantom geometries. The resulting dose profiles for the different phantoms and beam energies were compared. It was found that the 225 kVp photon beams have a better conformai dose distribution than the 100 kVp. The bone doses in the heterogeneous mouse phantom were about 4 – 5 (100 kVp) and 2 (225 kVp) times higher when compared to the homogeneous phantom. However, the lung dose does not vary significantly between the heterogeneous, homogeneous and bone homogeneous phantoms for either the 100 or 225 kVp photon beams. We concluded that bone dose enhancement was found when 100 and 225 kVp photon beams were used in small‐animal irradiation. This dosimetric effect due to the presence of the bone heterogeneity was more significant than the lung heterogeneity, and such bone dose enhancement does not occur in the typical patient's radiotherapy using the MV photon beams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 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".