Physical dose validation of dynamic treatment for Gamma Knife radiosurgery
Bibliographic record
Abstract
Abstract Background Dynamic treatment in Gamma Knife (GK) radiosurgery systems delivers radiation continuously with couch movement, as opposed to stationary step‐and‐shoot treatment where radiation is paused when moving between isocenters. Previous studies have shown the potential for dynamic GK treatment to give faster treatment times and improved dose conformity and homogeneity. However, these studies focused only on computational simulations and lack physical validation. Purpose This study aims conduct dynamic treatment dosimetric validation with physical experimental measurements. The experiments aim to (1) address assumptions made with computational studies, such as the validity of treating a continuous path as discretised points, (2) investigate uncertainties in translating computed plans to actual treatment, and (3) determine ideal treatment planning parameters, such as interval distance for the path discretization, collimator change limitations, and minimum isocenter treatment times. Methods This study uses a GK ICON treatment delivery machine, and a motion phantom custom‐made to attach to the machine's mask adapter and move in 1D superior‐inferior motion. Phantom positioning is first verified through comparisons against couch motion and computed doses. For dynamic treatment experiments, the phantom is moved through a program that first reads the desired treatment plan isocenters' position, time, and collimator sizes, then carries out the motion continuously while the treatment machine delivers radiation. Measurements are done with increasing levels of complexity: varying speed, varying collimator sizes, varying both speed and collimator sizes, then extends the same measurements to simulated 2D motion by combining phantom and couch motion. Dose comparisons between phantom motion radiation measurements and either couch motion measurements or dose calculations are analyzed with 2 mm/2% and 1 mm/2% gamma indices, using both local and global gamma index calculations. Results Phantom positional experiments show a high accuracy, with global gamma indices for all dose comparisons 99%. Discretization level to approximate continuous path as discrete points show the good dose matches with dose calculations when using 1 and 2‐mm gaps. Complex 1D motion, including varying speed, collimator sizes, or both, as well as 2D motion with the same complexities, all show good dose matches with dose calculations: the scores are 92.0% for the strictest 1 mm/2% local gamma index calculation, 99.8% for 2 mm/2% local gamma index, and 97.0% for all global gamma indices. Five simulated 2D treatments with optimized plans scored highly as well, with all gamma index scores 95.3% when compared to stationary treatment, and scores 97.9% when compared to plan calculated dose. Conclusions Dynamic treatment computational studies are validated, with dynamic treatment shown to be physically feasible and deliverable with high accuracy. A 2‐mm discretization level in treatment planning is proposed as the best option for shorter dose calculation times while maintaining dose accuracy. Our experimental method enables dynamic treatment measurements using the existing clinical workflow, which may be replicated in other centers, and future studies may include 2D or 3D motion experiments, or planning studies to further quantify potential indication‐specific benefits.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".