Feasibility of radiosurgery dosimetry using NIPAM 3D dosimeters and x-ray CT
Bibliographic record
Abstract
Abstract We investigated the feasibility of using N-isopropylacrylamide (NIPAM) dosimeters with x-ray CT to verify radiosurgery dose. Dosimeters were prepared at one facility and shipped to a second facility for irradiation. A simulation CT was acquired and plans prepared for a 4 field box, and a 4 arc VMAT radiosurgery plan to 6 targets with 1cm diameter. Each dosimeter was aligned via CBCT and irradiated, followed by 5 diagnostic CTs acquired after >24 hours, which were averaged for analysis. Absolute dose calibration was applied and dose evaluated for both plans. Hounsfield Units were proportional to dose above 10-12Gy. For the 4-field box, mean difference between measured and predicted dose >10Gy was -0.13Gy ±1.69Gy and gamma index was <1 for 72% and 65% of voxels using a 5% / 1mm and 3% / 2mm criteria, respectively (threshold = 15Gy, global dose criteria). For the multifocal SRS case, mean dose within each target was within -0.14Gy± 0.55Gy of the expected value, and gamma index was < 1 for 94.0% and 99.5% of voxels, respectively (threshold = 15Gy). NIPAM based 3D dosimetry with x-ray CT is well suited for validating radiosurgery spatial alignment, as well as dose distributions when dose is above 10-12Gy.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".