A comparison of dose warping methods for 4D Monte Carlo dose calculations in lung
Bibliographic record
Abstract
Dose calculation methods which incorporate tissue motion are an important tool for evaluating the effect of respiratory motion on the delivered dose distribution. 4D dose calculation methods use a sum of remapped doses calculated on 4D CT images of the patient at different respiratory phases to determine the cumulative dose received over the entire respiratory cycle. A number of methods for remapping the dose to the reference phase have been proposed, including center-of-mass (COM) tracking and trilinear (TL) interpolation. In this work we compare calculations of dose distributions remapped between extreme breathing phases against a 4D Monte Carlo dose code defDOSXYZ for three planning scenarios. No clinically significant differences were noted between dose distributions calculated by the three methods with the exception of an extreme motion evaluation case where TL and COM remapping underestimated the 95% target dose coverage by up to 16%. The accuracy of these dose calculation methods is significantly affected by the continuity of the deformation fields from non-linear image registration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".