Dosimetric evaluation of a dedicated stereotactic linear accelerator using measurement and Monte Carlo simulation
Bibliographic record
Abstract
Dosimetric parameters of a dedicated stereotactic linear accelerator have been investigated using measurements and Monte Carlo simulations. This linac has a unique built in multileaf collimation (MLC) system with the maximum opening of 16 x 21 cm2 and 4 mm leaf width at the isocenter and has successfully been modeled for the first time using the Monte Carlo simulation. The high resolution MLC, combined with its relatively large maximum field size, opens up a new opportunity for expanding stereotactic radiation treatment techniques from traditionally treating smaller targets to larger ones for both cranial and extracranial lesions. Dosimetric parameters of this linac such as accuracy of leaf positioning and field shaping, leakage and transmission, percentage depth doses, off-axes dose profiles, and dose penumbras were measured and calculated for different field sizes, depths, and source to surface distances. In addition, the ability of the linac in accurate dose delivery of several treatment plans, including intensity modulated radiation therapy (IMRT), performed on phantom and patients was determined. Ionization chamber, photon diode detector, films, several solid water phantoms, and a water tank were used for the measurements. The MLC leaf positioning to any particular point in the maximum aperture was accurate with a standard deviation of 0.29 mm. Maximum and average leakages were 1.7% and 1.1% for the reference field of 10.4 x 9.6 cm2. Measured penumbra widths (80%-20%) for this field at source axis distance (SAD) of 100 cm at a depth of 1.5 cm (dmax) were 3.2 and 4 mm for the leaf-sides and leaf-ends, respectively. The corresponding results at 10 cm depth and SAD =100 cm were 5.4 and 6.3 mm. Monte Carlo results generally agreed with the measurements to within 1% and or 1 mm, with respective uncertainties of 0.5% and 0.2 mm. The linac accuracy in delivering non-IMRT treatment plans was better than 1%. Ionization chamber dosimetry results for a phantom IMRT plan in the high dose and low dose regions were -0.5% and +3.6%, respectively. Dosimetry results at isocenter for three patients' IMRT plans were measured to be within 3% of their corresponding treatment plans. Film dosimetry was also used to compare dose distributions of IMRT treatment plans and delivered cumulative doses at different cross sectional planes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".