Design and dosimetry of a few leaf electron collimator for energy modulated electron therapy
Bibliographic record
Abstract
Despite the capability of energy modulated electron therapy (EMET) to achieve highly conformal dose distributions in superficial targets it has not been widely implemented due to problems inherent in electron beam radiotherapy such as planning dosimetry accuracy, and verification as well as a lack of systems for automated delivery. In previous work we proposed a novel technique to deliver EMET using an automated "few leaf electron collimator" (FLEC) that consists of four motor-driven leaves fit in a standard clinical electron beam applicator. Integrated with a Monte Carlo based optimization algorithm that utilizes patient-specific dose kernels, a treatment delivery was incorporated within the linear accelerator operation. The FLEC was envisioned to work as an accessory tool added to the clinical accelerator. In this article the design and construction of the FLEC prototype that match our compact design goals are presented. It is controlled using an in-house developed EMET controller. The structure of the software and the hardware characteristics of the EMET controller are demonstrated. Using a parallel plate ionization chamber, output measurements were obtained to validate the Monte Carlo calculations for a range of fields with different energies and sizes. Further verifications were also performed for comparing 1-D and 2-D dose distributions using energy independent radiochromic films. Comparisons between Monte Carlo calculations and measurements of complex intensity map deliveries show an overall agreement to within +/- 3%. This work confirms our design objectives of the FLEC that allow for automated delivery of EMET. Furthermore, the Monte Carlo dose calculation engine required for EMET planning was validated. The result supports the potential of the prototype FLEC for the planning and delivery of EMET.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".