Dosimetric IMRT verification with a flat‐panel EPID
Bibliographic record
Abstract
A convolution-based calibration procedure has been developed to use an amorphous silicon flat-panel electronic portal imaging device (EPID) for accurate dosimetric verification of intensity-modulated radiotherapy (IMRT) treatments. Raw EPID images were deconvolved to accurate, high-resolution 2-D distributions of primary fluence using a scatter kernel composed of two elements: a Monte Carlo generated kernel describing dose deposition in the EPID phosphor, and an empirically derived kernel describing optical photon spreading. Relative fluence profiles measured with the EPID are in very good agreement with those measured with a diamond detector, and exhibit excellent spatial resolution required for IMRT verification. For dosimetric verification, the EPID-measured primary fluences are convolved with a Monte Carlo kernel describing dose deposition in a solid water phantom, and cross-calibrated with ion chamber measurements. Dose distributions measured using the EPID agree to within 2.1% with those measured with film for open fields of 2 x 2 cm2 and 10 x 10 cm2. Predictions of the EPID phantom scattering factors (SPE) based on our scatter kernels are within 1% of the SPE measured for open field sizes of up to 16 x 16 cm2. Pretreatment verifications of step-and-shoot IMRT treatments using the EPID are in good agreement with those performed with film, with a mean percent difference of 0.2 +/- 1.0% for three IMRT treatments (24 fields).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".