Stray light in cone beam optical computed tomography: I. Measurement and reduction strategies with planar diffuse source
Bibliographic record
Abstract
Optical cone-beam computed tomographic (CBCT) scanning of 3D radiochromic dosimeters may provide a practical method for 3D dose verification in radiation therapy. However, in cone-beam geometry stray light contaminates the projection images, degrading the accuracy of reconstructed linear attenuation coefficients. Stray light was measured using a beam pass aperture array (BPA) and structured illumination methods. The stray-to-primary ray ratio (SPR) along the central axis was found to be 0.24 for a 5% gelatin hydrogel, representative of radiochromic hydrogels. The scanner was modified by moving the spectral filter from the detector to the source, changing the light's spatial fluence pattern and lowering the acceptance angle by extending distance between the source and object. These modifications reduced the SPR significantly from 0.24 to 0.06. The accuracy of the reconstructed linear attenuation coefficients for uniform carbon black liquids was compared to independent spectrometer measurements. Reducing the stray light increased the range of accurate transmission readings. In order to evaluate scanner performance for the more challenging application to small field dosimetry, a carbon black finger gel phantom was prepared. Reconstructions of the phantom from CBCT and fan-beam CT scans were compared. The modified source resulted in improved agreement. Subtraction of residual stray light, measured with BPA or structured illumination from each projection further improved agreement. Structured illumination was superior to BPA for measuring stray light for the smaller 1.2 and 0.5 cm diameter phantom fingers. At the costs of doubling the scanner size and tripling the number of scans, CBCT reconstructions of low-scattering hydrogel dosimeters agreed with those of fan-beam CT scans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".