SU‐E‐T‐676: Dosimetric Dependence on Variations of the Lung Density and Geometry: A Monte Carlo Evaluation Using Virtual Dynamic Heterogeneous Phantom
Bibliographic record
Abstract
Purpose: This Monte Carlo study investigates the dosimetry of the lung—soft tissue interface under different lung geometries using virtual dynamic heterogeneous phantoms. Methods: 6 and 18 MV photon beams (field sizes = 4×4 and 10×10 cm2), produced by a Varian 21EX linac were used. Three lung phantoms namely slab, column and cube representing different lung geometries were irradiated by the photon beams. The lung volume and density were varied in five phases mimicking the lung motion in breathing. The lung density was set to 0.15, 0.2, 0.25, 0.3 and 0.35 g/cc corresponding to phases 1 to 5 with the lung volume changed while keeping the lung mass constant. Relative depth doses and beam profiles for each phantom in the five phases were calculated using the DOSXYZnrc. Results: Our results show that there is obvious dosimetric variation around the lung—soft tissue interface among the five phases in the three lung phantoms. This is due to the positional and density change of the interface. Interpolation was done to register all dose distributions in the five phases to one in order to compare with the dosimetry of a static lung phantom (e.g. only phase 3 with lung density = 0.25 g/cc). Dose deviation was found to be about ±5%, when a breathing lung with changes of volume and density was considered. Moreover, the deviation is more significant for photon beams of relatively higher energy (18 MV) and smaller field size (4×4 cm2). Conclusions: Through Monte Carlo studies of lung heterogeneous correction, we concluded that more realistic dosimetry may be acquired by considering a virtual dynamic phantom to mimic the breathing motion. The accuracy of the lung motion can further be improved by increasing the number of phase and applying weight factor to each phase in the breathing cycle.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".