An algorithm for systematic selection of beam directions for IMRT
Bibliographic record
Abstract
Selection of the number of beams and their directions can be an important problem in radiation therapy, especially when a tumor surrounds a critical organ or is surrounded by multiple critical organs. Beam directions, in this sense, are chosen to not only avoid critical organs, but also to achieve better target dose uniformity. In intensity-modulated radiation therapy (IMRT), optimization of beam directions is further complicated due to the dependence of one beam direction on its corresponding beamlet intensities and the beamlet intensities of all other beam directions. The result is an excessively enlarged search space, even when the number of beams is small (two to three). Until now, only a handful of publications exist regarding beam direction optimization in IMRT. Here, we report a new systematic approach that determines a suitable number of "more optimal" beam directions without optimizing a complicated objective function or resorting to brute force. We start by assuming that beam directions chosen for an N-beam plan are candidates for beam directions in the search for an (N + 1)-beam plan. Knowing that beam directions in an N-beam plan are not always the best choices for the (N + 1)-beam plan, we introduce into the beam direction selection process an analysis of the beamlet weights of every beam direction set sampled. If the relative weights of any particular beam compared to other beams are insignificant and hence have no significant effect on the quality of the treatment plan, then we eliminate this beam from the plan. The algorithm terminates basically when the relative weights of the last beam compared to other beams are insignificant or the replacement of an eliminated beam does not improve the plan. This concept was applied to three two-dimensional phantoms and each plan was compared to a standard equally spaced IMRT plan in terms of dose distributions, dose-volume histograms, and objective function values. The results show improvements in both target dose uniformity and critical organ sparing often with a fewer number of beams than standard equally spaced beam plans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".