Experimental verification of 4D Monte Carlo simulations of dose delivery to a moving anatomy
Bibliographic record
Abstract
Purpose To evaluate a novel 4D Monte Carlo simulation tool by comparing calculations to physical measurements using a respiratory motion phantom. Methods We used a dynamic Quasar phantom in both stationary and breathing states (sinusoidal motion of amplitude of 1.8 cm and period of 3.3 s) for dose measurements on an Elekta Agility linear accelerator. Gafchromic EBT3 film and the RADPOS 4D dosimetry system were placed inside the lung insert of the phantom to measure dose profiles and point‐dose values at the center of the spherical tumor inside the insert. Both a static 4 × 4 cm2 field and a VMAT plan were delivered. Static and 4D Monte Carlo simulations of the treatment deliveries were performed using DOSXYZnrc and a modified version of the defDOSXYZnrc user code that allows modeling of the continuous motion of both machine and patient. DICOM treatment plan files and linac delivery log files were used to generate corresponding input files. The phantom motion recorded by RADPOS during beam delivery was incorporated into the input files for the 4DdefDOSXYZnrc simulations. Results For stationary phantom simulations, all point‐dose values from MC simulations at the tumor center agreed within 1% with film and within 2% with RADPOS. More than 98% of the voxels from simulated dose profiles passed a 1D gamma of 2%/2‐mm criteria against measured dose profiles. Similar results were observed when applying a 2D gamma analysis with a 2%/2‐mm criteria to compare 2D dose distributions of Monte Carlo simulations against measurements. For simulations on the moving phantom, MC‐calculated dose values at the center of the tumor were found to be within 1% of film and within 2σ of experimental uncertainties which are 2.8% of the RADPOS measurements. 1D gamma comparisons of the dose profiles were better than 91%, and 2D gamma comparisons of the 2D dose distributions were found to be better than 94%. Conclusion Our 4D Monte Carlo method using defDOSXYZnrc can be used to accurately calculate the dose distribution in continuously moving anatomy for various treatment techniques. This work, if extended to deformable anatomies, can be used to reconstruct patient delivered dose for use in adaptive radiation therapy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".