A novel time dependent gamma evaluation function for dynamic 2D and 3D dose distributions
Bibliographic record
Abstract
Modern external beam radiotherapy requires detailed verification and quality assurance so that confidence can be placed on both the delivery of a single treatment fraction and on the consistency of delivery throughout the treatment course. To verify dose distributions, a comparison between prediction and measurement must be made. Comparisons between two dose distributions are commonly performed using a Gamma evaluation which is a calculation of two quantities on a pixel by pixel basis; the dose difference, and the distance to agreement. By providing acceptance criteria (e.g. 3%, 3 mm), the function will find the most appropriate match within its two degrees of freedom. For complex dynamic treatments such as IMRT or VMAT it is important to verify the dose delivery in a time dependent manner and so a gamma evaluation that includes a degree of freedom in the time domain via a third parameter, time to agreement, is presented here. A C++ (mex) based gamma function was created that could be run on either CPU and GPU computing platforms that would allow a degree of freedom in the time domain. Simple test cases were created in both 2D and 3D comprising of simple geometrical shapes with well-defined boundaries varying over time. Changes of varying magnitude in either space or time were introduced and repeated gamma analyses were performed varying the criteria. A clinical VMAT case was also included, artificial air bubbles of varying size were introduced to a patient geometry, along with shifts of varying magnitude in treatment time. For all test cases where errors in distance, dose or time were introduced, the time dependent gamma evaluation could accurately highlight the errors.The time dependent gamma function presented here allows time to be included as a degree of freedom in gamma evaluations. The function allows for 2D and 3D data sets which are varying over time to be compared using appropriate criteria without penalising minor offsets of subsequent radiation fields in time.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".