Monte Carlo simulation on pre-clinical irradiation: A heterogeneous phantom study on monoenergetic kilovoltage photon beams
Bibliographic record
Abstract
This study investigated radiation dose variations in pre-clinical irradiation due to the photon beam energy and presence of tissue heterogeneity. Based on the same mouse computed tomography image dataset, three phantoms namely, heterogeneous, homogeneous and bone homogeneous were used. These phantoms were generated by overriding the relative electron density of no voxel (heterogeneous), all voxel (homogeneous) and the bone voxel (bone homogeneous) to one. 360° photon arcs with beam energies of 50 – 1250 keV were used in mouse irradiations. Doses in the above phantoms were calculated using the EGSnrc-based DOSXYZnrc code through the DOSCTP. Monte Carlo simulations were carried out in parallel using multiple nodes in a high-performance computing cluster. It was found that the dose conformity increased with the increase of the photon beam energy from the keV to MeV range. For the heterogeneous mouse phantom, increasing the photon beam energy from 50 keV to 1250 keV increased seven times the dose deposited at the isocenter. For the bone dose enhancement, the mean dose was 2.7 times higher when the bone heterogeneity was not neglected using the 50 keV photon beams in the mouse irradiation. Bone dose enhancement affecting the mean dose was found in the photon beams with energy range of 50 – 200 keV and the dose enhancement decreased with an increase of the beam energy. Moreover, the MeV photon beam had a higher dose at the isocenter, and a better dose conformity compared to the keV beam.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".