SU‐E‐T‐376: Incorporating Photon Beamlet Energy in Optimization of Intensity Modulated Beams
Bibliographic record
Abstract
Purpose: To develop an energy dependent optimization criteria for incorporation into the intensity modulated radiation therapy (IMRT) optimization problem and perform simultaneous optimization of photon energy and intensity for X‐ray modulated radiotherapy (XMRT). Methods: For a simple spherical tumor model, energy dependent optimization criteria (EDOC) were formulated based on three objectives: maximize distal edge dose of the tumor, minimize healthy tissue entrance dose, and maximize uniformity of the tumor dose distribution. This was implemented in MATLAB to test on a simplified scenario of a 10 cm wide 1D photon beam incident on a spherical tumor (10cm radius) centered within a 5600 cm3 water phantom. The 1D beam was further partitioned into 20 beamlets of equal width. From the MATLAB program, an optimal assignment of monoenergetic photons (ranging from 1 MeV to 18 MeV) for each beamlet based on the EDOC was obtained. The dose calculation for each monoenergetic photon beamlet was restricted to primary dose only. Results: The beamlet distributions showed the mid tumor region assigned higher energies due to the large amount of tumor tissue traversed. Outer edge beamlets were assigned lower energies to spare the large portion of healthy tissue traversed. Changing priorities for each objective effected the optimal distribution in an intuitive manner (i.e. larger emphasis on sparing healthy tissue results in lower energies assigned to each beamlet). Conclusion: A viable set of energy dependent optimization criteria has been developed that can be used in reformulating the inverse optimization problem for radiotherapy, allowing optimization in terms of both beam energy and intensity. Currently, geant4 is being used to generate and benchmark dose profiles for 1 MeV to 18 MeV photons energies to improve the accuracy of the dose calculation. A rigorous testing of the optimization criteria through added complexity of radiotherapy is currently being investigated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".