Evaluation of an x-ray CT polymer gel dosimetry system in the measurement of deformed dose
Bibliographic record
Abstract
Abstract This study is an evaluation of the use of a N-isopropylacrylamide (NIPAM)-based x-ray CT polymer gel dosimetry (PGD) system in the measurement of deformed dose. This work also compares dose that is measured by the gel dosimetry system to dose calculated by a novel deformable dose accumulation algorithm, defDOSXYZnrc, that uses direct voxel tracking. Deformable gels were first irradiated using a single 3.5 × 5 cm 2 open field and the static dose was compared to defDOSXYZnrc as a control measurement. Gel measurement was found to be in excellent agreement with defDOSXYZnrc in the static case with gamma passing rates of 94.5% using a 3%/3 mm criterion and 93.3% using a 3%/2 mm criterion. Following the static measurements, a deformable gel was irradiated with the same single field under an external compression of 25 mm and then released from this compression for dosimetric read out. The measured deformed dose was then compared to deformed dose calculated by defDOSXYZnrc based on deformation vectors produced by the Velocity AI deformable image registration (DIR) algorithm. In the deformed dose distribution there were differences in the measured and calculated field position of up to 0.8 mm and differences in the measured in calculated field size of up to 11.9 mm. Gamma pass rates were 60.0% using a 3%/3 mm criterion and 56.8% using a 3%/2 mm criterion for the deforming measurements representing a decrease in agreement compared to the control measurements. Further analysis showed that passing rates increased to 86.5% using a 3%/3 mm criterion and 70.5% using a 3%/2 mm criterion in voxels within 5 mm of fiducial markers used to guide the deformable image registration. This work represents the first measurement of deformed dose using x-ray CT polymer gel dosimetry. Overall these results highlight some of the challenges in the calculation and measurement of deforming dose and provide insight into possible strategies for improvement.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".