Sci‐Fri AM: YIS‐04: Scatter correction of cone beam optical computed tomography for polymer gel dosimetry
Bibliographic record
Abstract
Optical computed tomography (OptCT) may become the preferred scanning method for gel dosimetry dose validations, due to its high sensitivity and relatively low cost. Cone beam computed tomography (CBCT) arrangements are advantageous because of reduced scan time. However, CBCT arrangements are more sensitive to errors associated with scatter than other CT configurations. Unfortunately in polymer gel dosimetry this problem is amplified as the primary mode of beam attenuation is through scatter. Thus, managing and reducing the effects of scatter remains an important challenge for cone beam OptCT. In this work we examine two schemes for reducing the effects of scatter in the Vista cone beam OptCT system. First, we employed a pair of anti-scatter polarizing planes to reduce the magnitude of stray light reaching the camera. Secondly, we implemented a beam stop array (BSA) sampling method -which has been successful in correcting for scatter in X-ray CBCT- to obtain scatter field measurements that are subtracted from CT projections removing veiling glare. While both implementations reduced scatter related artifacts, the BSA technique yielded greater improvement without obvious image degradation. Comparative studies between absorbing dye standards and colloidal scattering standards with the same spectrophotometric optical attenuation revealed that application of the BSA technique nullified OptCT measurement disagreements between scattering and absorbing systems. When the BSA scatter correction technique was applied to polymer gel dosimetry 3%3mm agreement rose from 79.2% to 99.82%. These findings underscore the strength of the BSA sampling technique and its utility in cone beam OptCT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.276 | 0.090 |
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".