CT gel dosimetry technique: Comparison of a planned and measured 3D stereotactic dose volume
Bibliographic record
Abstract
This study presents a 3D dose mapping of complex dose distributions using an x-ray computed tomography (CT) polymer gel dosimetry technique. Two polyacrylamide gels (PAGs) of identical composition were irradiated with the same four arc stereotactic treatment to maximum doses of 15 Gy (PAG1) and 8 Gy (PAG2). The PAGs were CT imaged using a previously defined protocol that involves image averaging and background subtraction to improve image quality. For comparison with the planned isodose distribution, the PAG images were converted to relative dose maps using a CT number-dose calibration curve or simple division. The PAG images were then co-registered with the planning CT images in the BrainLab treatment planning software which automatically provides reconstructed sagittal and coronal images for 3D evaluation of measured and planned dose. The hypo-intense high dose region in both sets of gel images agreed with the planned 80% isodose contour and was shifted by up to 1.5 and 3.0 mm in the axial and reconstructed planes, respectively. This demonstrates the ability of the CT gel technique to accurately localize the high dose region produced by the stereotactic treatment. The resulting agreement of the measured relative dose volume for PAG1 was within 3.0 mm for the 50% and 80% isodose surfaces. However, the dose contrast was too low in PAG2 to allow for accurate definition of measured relative dose surfaces. Thus, a PAG should be irradiated to higher doses if quantitative relative dose information is required. Unfortunately, this implies use of an additional PAG and its CT number dose response since doses greater than 8-10 Gy fall outside the linear regions of the response.
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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) | 0.001 | 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 | 0.000 |
| 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".