Poster - Thurs Eve-19: IGRT QA for helical tomotherapy
Bibliographic record
Abstract
Helical tomotherapy (HT) with daily imaging using mega-voltage computed tomography for 3D image guided radiotherapy (IGRT). We present two techniques developed in our department to verify the integrity of the HT IGRT process. A phantom was constructed of two equally sized (5×10×10cm3) polystyrene blocks stacked on top of each other, each piece having a hole capable of receiving a small volume ionization chamber. A piece of Radiochromic film fits neatly in between the blocks. The phantom was CT scanned and the CT slices were transferred to the HT treatment planning system (TPS). The first procedure is used daily to test the image registration aspects of the IGRT process, and involves setting the phantom on the tomotherapy treatment unit table in an arbitrary position, imaging it, and performing image registration to determine what displacements are necessary to return the phantom to the planned position. A variation of this test is to place the phantom at a position incurring known displacements and ensuring the registration recognizes the shifts. The second procedure verifies the entire IGRT procedure, and includes the first procedure and the delivery of a treatment plan. An inverse plan is created to deliver simultaneously 2 and 3 Gy to 2 pre-defined targets. The treatment plan can be setup as a QA plan in the TPS software, allowing for a detailed comparison of ion chamber measurements and film dosimetry to the planned dose distribution. We have found that these QA procedures adequately test the IGRT capabilities of our HT unit.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.060 | 0.014 |
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".