Sci‐PM Sat ‐ 06: Investigating dose resolution in CT polymer gel dosimetry
Bibliographic record
Abstract
X‐ray computed tomography (CT) is a novel method of extracting 3D dose information from irradiated polymer gels and presents an exciting possibility for widespread clinical application of gel dosimetry. However, clinical use remains limited due, in part, to poor dose resolution. This work investigates dose resolution in CT polyacrylamide gel (PAG) dosimetry and provides optimization strategies for improving dose resolution. In addition, using current PAG formulations, achievable dose resolution is calculated for a range of voxel sizes given a 1 hour imaging time constraint. Phantom design, imaging protocol and voxel size are all shown to be important considerations for improving dose resolution due to their effects on image noise. Specific recommendations include: minimizing phantom size, maximizing tube voltage, using standard or soft reconstruction algorithms and increasing voxel size where clinically appropriate. Dose resolution also depends on the sensitivity and dose range of the gel CT to dose response and for PAGs a 50%C (cross‐linking fraction) gel is shown to provide the best dose resolution. With technique optimization dose resolutions of < 3% for region of interest imaging and ∼ 5% for volume imaging (2.5×2.5×3mm3 voxel size) can be obtained. These results approach those of MRI and OCT gel dosimetry within a shorter (⩽ 1hr) imaging time and highlight the potential for CT gel dosimetry to be a clinically successful 3D dosimetry tool. Future work remains in developing gels more sensitive to CT read‐out and implementing multi‐slice CT imaging, advances which stand to further increase the quality of CT gel dosimetry.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".