Three-dimensional IMRT verification with a flat-panel EPID
Bibliographic record
Abstract
A three-dimensional (3D) intensity-modulated radiotherapy (IMRT) pretreatment verification procedure has been developed based on the measurement of two-dimensional (2D) primary fluence profiles using an amorphous silicon flat-panel electronic portal imaging device (EPID). As described in our previous work, fluence profiles are extracted from EPID images by deconvolution with kernels that represent signal spread in the EPID due to radiation and optical scattering. The deconvolution kernels are derived using Monte Carlo simulations of dose deposition in the EPID and empirical fitting methods, for both 6 and 15 MV photon energies. In our new 3D verification technique, 2D fluence modulation profiles for each IMRT field in a treatment are used as input to a treatment planning system (TPS), which then generates 3D doses. Verification is accomplished by comparing this new EPID-based 3D dose distribution to the planned dose distribution calculated by the TPS. Thermoluminescent dosimeter (TLD) point dose measurements for an IMRT treatment of an anthropomorphic phantom were in good agreement with the EPID-based 3D doses; in contrast, the planned dose under-predicts the TLD measurement in a high-gradient region by approximately 16%. Similarly, large discrepancies between EPID-based and TPS doses were also evident in dose profiles of small fields incident on a water phantom. These results suggest that our 3D EPID-based method is effective in quantifying relevant uncertainties in the dose calculations of our TPS for IMRT treatments. For three clinical head and neck cancer IMRT treatment plans, our TPS was found to underestimate the mean EPID-based doses in the critical structures of the spinal cord and the parotids by approximately 4 Gy (11%-14%). According to radiobiological modeling calculations that were performed, such underestimates can potentially lead to clinically significant underpredictions of normal tissue complication rates.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".