MO‐E‐BRA‐02: Computer Aided Design and Monte Carlo Validation of a Patient‐Specific Co‐60 TBI Treatment Unit
Bibliographic record
Abstract
Purpose: To modify and characterize a teletherapy 60Co unit for total body irradiation (TBI) treatments at extended SSD using experiments and a Monte Carlo (MC) model and to propose the design of custom compensators based on MC dose in the patient. Methods and Materials: An existing Eldorado T78 60Co teletherapy unit was stripped from its original collimator and equipped with beam‐defining cerrobend blocks for extended SSD TBI treatments. An acrylic flattening filter was numerically designed based on detailed mapping of the dose distribution of the large open field at 10 cm depth in water and using a primary radiation attenuation calculation. An EGSnrc MC model of the resulting unit was developed and validated. Dose distributions measured using ionization chambers were compared to MC dose distribution in air and phantom. The validated model was used to calculate dose in a whole‐body patient CT image. A compensator, designed to make the patient mid‐plane dose uniform was proposed. Results: The designed filter flattens the beam to within ±2% over an area of 200 × 70 cm2 at patient mid‐plane. The agreement between measured and calculated dose profiles in the open and the filtered beams is at the sub 2% and sub 1% level, respectively. Surface dose in the filtered beam is 78.5% and mean photon energy of the primary fluence is 0.94 MeV independent of position in the field. Patient‐specific calculations show excess dose in lung and the area of neck and extremities relative to prescription dose, by up to 20% and 30%, respectively. These excess doses can be reduced by introducing compensators, designed from the mid‐plane dose distribution, following similar techniques as for the design of the filter. Conclusions: This work shows that extended SSD 60Co irradiation equipment and patient‐specific compensators designed based on realistic dose distributions, can improve TBI delivery.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".