Two‐dimensional <i>in vivo</i> dose verification using portal imaging and correlation ratios
Bibliographic record
Abstract
The electronic portal imaging device (EPID) has the potential to be used for in vivo dosimetry during radiation therapy as an additional dose delivery check. In this study we have extended a method developed by A. Piermattei and colleagues in 2006 that made use of EPID transit images (acquired during treatment) to calculate dose in the isocenter point. The extension allows calculation of two-dimensional dose maps of the entire radiation field at the depth of isocenter. We quantified the variability of the ratio of EPID signal to dose in the isocenter plane in Solid Water phantoms of various thicknesses and with various field sizes, and designed a field edge dose calculation correction. To validate the method, we designed three realistic conventional radiation therapy treatment plans on a thorax and head anthropomorphic phantom (whole brain, brain primary, lung tumor). Using CT data, EPID transit images, EPID signal-to-dose correlation, and our edge correction, we calculated dose in the isocenter plane and compared it with the treatment planning system's prediction. Gamma evaluation (3%, 3 mm) showed good agreement (Pγ<1 ≥ 96.5%) for all fields of the whole brain and brain primary plans. In the presence of lung, however, our algorithm overestimated dose by 7%-9%. This 2D EPID-based in vivo dosimetry method can be used for posttreatment dose verification, thereby improving the safety and quality of patient treatments. With future work, it may be extended to measure dose in real time and to prevent harmful delivery errors.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".