The effects of dose calculation resolution on dose accuracy for radiation therapy treatments of the lung. Part I. A Monte Carlo model of the lung
Bibliographic record
Abstract
PURPOSE: The purpose of this work was to create an anatomically detailed EGSnrc Monte Carlo based model of the right lung. The resulting model, called BRANCH, includes an accurate representation of the right bronchial, arterial, and venous branching networks down to a scale of 0.1 mm. The model may be varied to represent lung shape and density at any phase of the respiration cycle. METHODS: Polynomial surfaces were used to approximate the anatomic boundaries that define the right lung surface at several phases of the respiration cycle. A branching network algorithm was used to generate the bronchial, arterial, and venous trees within the anatomic boundaries. The branching networks were modeled as a series of bifurcating cylinders connected by spherical junctions. The validity of the BRANCH dose calculation was verified using an all-water version of the model. RESULTS: The geometric dimensions of the BRANCH model corresponded well with published data. The bronchial tree model contained 27 798 branches ranging from 0.02 to 0.54 cm in diameter. The arterial tree model had 27,957 branches ranging from 0.02 to 1.2 cm in diameter. The venous model tree had 26 347 branches ranging from 0.02 to 0.34 cm in diameter. A gamma analysis indicated that the all-water BRANCH Monte Carlo code produced dose distributions that agreed within 0.1 cm and 0.5% to conventional DOSXYZnrc results. CONCLUSIONS: The BRANCH model is a useful tool for performing detailed dosimetric studies within a realistic representation of the lung.
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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".