Simultaneous optimization of beam orientations, wedge filters and field weights for inverse planning with anatomy‐based MLC fields
Bibliographic record
Abstract
As an alternative between manual planning and beamlet-based IMRT, we have developed an optimization system for inverse planning with anatomy-based MLC fields. In this system, named Ballista, the orientation (table and gantry), the wedge filter and the field weights are simultaneously optimized for every beam. An interesting feature is that the system is coupled to Pinnacle3 by means of the PinnComm interface, and uses its convolution dose calculation engine. A fully automatic MLC segmentation algorithm is also included. The plan evaluation is based on a quasi-random sampling and on a quadratic objective function with penalty-like constraints. For efficiency, optimal wedge angles and wedge orientations are determined using the concept of the super-omni wedge. A bound-constrained quasi-Newton algorithm performs field weight optimization, while a fast simulated annealing algorithm selects the optimal beam orientations. Moreover, in order to generate directly deliverable plans, the following practical considerations have been incorporated in the system: collision between the gantry and the table as well as avoidance of the radio-opaque elements of a table top. We illustrate the performance of the new system on two patients. In a rhabdomyosarcoma case, the system generated plans improving both the target coverage and the sparing of the parotide, as compared to a manually designed plan. In the second case presented, the system successfully produced an adequate plan for the treatment of the prostate while avoiding both hip prostheses. For the many cases where full IMRT may not be necessary, the system efficiently generates satisfactory plans meeting the clinical objectives, while keeping the treatment verification much simpler.
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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".