Dosimetry of small bone joint calculated by the analytical anisotropic algorithm: a Monte Carlo evaluation using the EGSnrc
Bibliographic record
Abstract
This study compared a small bone joint dosimetry calculated by the anisotropic analytical algorithm (AAA) and Monte Carlo simulation using megavoltage (MV) photon beams. The performance of the AAA in the joint dose calculation was evaluated using Monte Carlo simulation, and dependences of joint dose on its width and beam angle were investigated. Small bone joint phantoms containing a vertical water layer (0.5-2 mm) sandwiched by two bones (2 × 2 × 2 cm3) were irradiated by the 6 and 15 MV photon beams with field size equal to 4 × 4 cm2. Depth doses along the central beam axis in a joint (cartilage) were calculated with and without a bolus (thickness = 1.5 cm) added on top of the phantoms. Different beam angles (0°-15°) were used with the isocenter set to the center of the bone joint for dose calculations using the AAA (Eclipse treatment planning system) and Monte Carlo simulation (the EGSnrc code). For dosimetry comparison and normalization, dose calculations were repeated in homogeneous water phantoms with the bone substituted by water. Comparing the calculated dosimetry between the AAA and Monte Carlo simulation, the AAA underestimated joint doses varying with its widths by about 6%-12% for 6 MV and 12%-23% for 15 MV without bolus, and by 7% for 6 MV and 13%-17% for 15 MV with bolus. Moreover, joint doses calculated by the AAA did not vary with the joint width and beam angle. From Monte Carlo results, there was a decrease in the calculated joint dose as the joint width increased, and a slight decrease as the beam angle increased. When bolus was added to the phantom, it was found that variations of joint dose with its width and beam angle became less significant for the 6 MV photon beams. In conclusion, dosimetry deviation in small bone joint calculated by the AAA and Monte Carlo simulation was studied using the 6 and 15 MV photon beam. The AAA could not predict variations of joint dose with its width and beam angle, which were predicted by the Monte Carlo simulations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".