Pre-treatment radiotherapy dose verification using Monte Carlo doselet modulation in a spherical phantom
Bibliographic record
Abstract
Due to the increasing complexity of radiotherapy delivery, accurate dose verification has become an essential part of the clinical treatment process. The purpose of this work was to develop a pre-treatment verification technique capable of quickly reconstructing 3D dose distributions from both coplanar and non-coplanar treatments. For each treatment field, electronic portal images were taken in non-transmission mode (with no patient in the beam) allowing the derivation of the delivered fluence maps. The dose reconstruction was then performed in a spherical water phantom by modulating and summing the Monte Carlo (MC) doselets, defined on a spherical co-ordinate system, and pre-calculated from azimuthally symmetric fluence above the jaws. The technique, called the spherical doselet modulation (SDM) method, essentially eliminates the statistical uncertainty of the MC dose calculations by exploiting the azimuthal symmetry in both a patient-independent phase-space and in a virtual spherical water phantom. For example, this symmetry allowed the number of doselets necessary for dose reconstruction to be reduced by a factor of ∼250. In this work, only 51 radially binned doselets were used (each generated from all particles in a given annulus of the phase-space, azimuthally redistributed into a small cylindrical sector). The SDM method mitigates the most computationally intensive part of this type of dose reconstruction--reading, weighting and summing dose matrices. The accuracy of the system was tested against MC calculations as well as our previously reported phase-space modulation method, using a series of open field and IMRT cases. The mean chi- and gamma-test 3%/3 mm success rates of the SDM method were 98.6% and 99.5%, respectively, when compared to full MC simulation. The total calculation time was 96 s per treatment field on a single processor core.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".