Linac-integrated kV-cone beam CT polymer gel dosimetry
Bibliographic record
Abstract
X-ray CT polymer gel dosimetry (PGD) remains a promising tool for three dimensional verification of high-dose treatment deliveries such as non-coplanar stereotactic irradiations. Recent demonstrations have shown a proof-of-principle application of linac-integrated cone beam CT-imaged (LI-CBCT) PGDs for 3D dose verification. LI-CBCT offers advantages over previous CT based PGD, including close to real-time imaging of the irradiated dosimeter, as well as the ability to maintain the dosimeter in the same physical location for irradiation and imaging, thereby eliminating spatial errors due to dosimeter re-positioning for read-out that may occur for other systems. However the dosimetric characteristics of a LI-CBCT PGD system remain to be established. The work herein determines the dosimetric properties and critical parameters needed to perform cone beam PGD. In particular, we show that imaging the dosimeter 20-30 min post irradiation offers excellent recovery of maximum polymerization yield ([Formula: see text]90%), averaging with as few as 10 image averages can provide ∼90% gamma pass rates (3%, 3 mm) as compared to treatment planning, and that eliminating outlier averaging points can improve the precision and signal to noise ratio of resultant images. In summary, with appropriate methodology LI-CBCT PGD can provide dosimetric data capable of verification of complex high dose radiation deliveries in three dimensions and may find use in commissioning and validation of novel complex treatments.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".