Sci-Fri PM: Delivery - 04: A Patient Scatter Model for In Vivo Radiation Therapy Verification Using EPID Dosimetry
Bibliographic record
Abstract
Radiation therapy has become increasingly complex with the introduction of new technologies like intensity modulated radiation therapy (IMRT) and rotational-IMRT. Thorough dosimetric verification is required to ensure sufficient tumour coverage and normal tissue sparing. Pretreatment verification is conventionally performed prior to a patient's course of treatment, but validation during treatment delivery does not usually occur. One method to determine whether the treatment was delivered correctly is through the comparison of a measured portal image (taken with an a-Si EPID) to a predicted portal dose image (modeling the same EPID). In this work, a patient scatter model was incorporated into an existing fluence model to account for the effect of a patient during an in vivo portal image measurement. The patient/phantom CT data set is converted to an equivalent homogeneous phantom (EHP). The modeled beam fluence is then ray-traced through the EHP and onto the EPID, accounting for patient attenuation. Patient scatter fluence is calculated through the superposition of a library of pre-calculated Monte Carlo scatter fluence kernels. The dose delivered to the EPID is determined with the convolution of a series of mono-energetic dose kernels. The patient scatter model was tested with slab phantoms for a range of thicknesses, air gaps and field sizes, and was found to accurately predict images within 2% and 3 mm. A prostate in vivo IMRT field prediction was also carried out, with a comparison of the relative images resulting in a model accuracy of 3% and 3 mm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".