Exploring the limits of spatial resolution in radiation dose delivery
Bibliographic record
Abstract
Flexibility and complexity in patient treatment due to advances in radiotherapy techniques necessitates a simple method for evaluating spatial resolution capabilities of the dose delivery device. Our purpose in this investigation is to evaluate a model that describes the ability of a radiation therapy device to deliver a desired dose distribution. The model is based on linear systems theory and is analogous to methods used to describe resolution degradation in imaging systems. A qualitative analysis of spatial resolution degradation using the model is presented in the spatial and spatial frequency domains. The ability of the model to predict the effects of geometric dose conformity to treatment volumes is evaluated by varying multileaf collimator leaf width and magnitude of dose spreading. Dose distributions for three clinical treatment shapes, circular shapes of varying diameter and one intensity modulated shape are used in the evaluation. We show that the model accurately predicts the dependence of dose conformity on these parameters. The spatial resolution capabilities of different radiation therapy devices can be quantified using the model, providing a simple method for comparing different treatment machine characteristics. Also, as different treatment sites have different resolution requirements this model may be used to tailor machine characteristics to the specific site.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".