Poster — Thur Eve — 51: Three‐dimensional in‐vivo EPID dosimetry of IMRT and VMAT treatments
Bibliographic record
Abstract
As radiation treatment delivery becomes more complex, including dynamic IMRT and VMAT, the argument for routine patient dose verification becomes more compelling. This work demonstrates a technique that utilizes our pre-existing portal dose image prediction algorithm to compute 3D patient dose from recorded on-treatment portal images. This approach can be applied on CT simulation data or daily cone-beam CT data sets. Here we demonstrate the robustness of our dose reconstruction technique with phantom and patient examples, with delivery schemes including IMRT and VMAT. For an example prostate treatment site, 3D dose distributions reconstructed in the patient model are computed for each fraction, and DVHs presented. Results indicate that the patient dose reconstruction algorithm compares well with treatment planning system computed doses for controlled test situations. For patient examples the 3D chi comparison values (similar to the gamma comparison) ranged from 94.5% to 100% agreement for voxels > 10% maximum dose for all treatments and phantom cases. We show an example where the DVH for fraction nine of a prostate treatment fails acceptability criteria, due to a previously unnoticed positioning error. Future work involves building our patient dose reconstruction into a QA package, subsequently integrating it into a clinical workflow. We are also investigating the use of this tool as a backbone for an in-house adaptive radiotherapy implementation. This work is supported by Varian Medical Systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".