Stray light in cone beam optical computed tomography: II. Reduction using a convergent light source
Bibliographic record
Abstract
Optical cone beam computed tomography (CBCT) using a broad beam and CCD camera is a fast method for densitometry of 3D optical gel dosimeters. However, diffuse light sources introduce considerable stray light into the imaging system, leading to underestimation of attenuation coefficients and non-uniformities in CT images unless corrections are applied to each projection image. In this study, the light source of a commercial optical CT scanner is replaced with a convergent cone beam source consisting of almost exclusively image forming primary rays. The convergent source is achieved using a small isotropic source and a Fresnel lens. To characterize stray light effects, full-field cone beam CT imaging is compared to fan beam CT (FBCT) using a 1 cm high fan beam aperture centered on the optic axis of the system. Attenuating liquids are scanned within a large 96 mm diameter uniform phantom and in a small 13.5 mm diameter finger phantom. For the uniform phantom, cone and fan beam CT attenuation coefficients agree within a maximum deviation of (1 ± 2)% between mean values over a wide range from 0.036 to 0.43 cm(-1). For the finger phantom, agreement is found with a maximum deviation of (4 ± 2)% between mean values over a range of 0.1-0.47 cm(-1). With the convergent source, artifacts associated with refractive index mismatch and vessel optical features are more pronounced. Further optimization of the source size to achieve a balance between quantitative accuracy and artifact reduction should enable practical, accurate 3D dosimetry, avoiding time consuming 3D scatter measurements.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".