Stray light in cone beam optical computed tomography: III. Evaluation of a redesigned large-volume commercial scanner based on a convergent light source
Bibliographic record
Abstract
Abstract Optical cone beam computed tomography (CT), using a digital camera to acquire 2D projection images, provides a fast, mechanically simple method for 3D radiation dosimetry. However, original cone beam designs had poor accuracy as a result of considerable scatter/stray light reaching the camera. Previously, our group presented a redesigned convergent light source for optical cone beam CT that considerably reduced stray light contribution and improved accuracy (Dekker et al 2016 Phys. Med. Biol . 61 2910). Here, we performed an evaluation of a newly updated commercial optical cone beam CT scanner (Vista TM , ModusQA, London, Canada) based on that design. Two different light source configurations were examined: the manufacturer’s default configuration which uses a 10 cm wide, 5 cm high diffuser light source, and a smaller, 1.5 cm diameter diffuser light source that more closely aligns with our previously described design. We imaged large volume (15 cm diameter cylinders) absorbing and scattering solution phantoms as well as a 1.25 cm diameter absorber placed within 15 cm diameter gel-like scattering phantom. Optical CT reconstructions were compared against narrow-beam measurements of attenuation made by placing an aperture in the optical CT system. Our results show that considerable stray light is present when using the manufacturer’s default configuration, as cupping artifacts and large (⩾10%) discrepancies between optical CT and narrow-beam attenuation measurements occur when imaging scattering phantoms. However, when imaging is performed using the 1.5 cm diameter source, optical CT measurements agree with narrow-beam measurements within ∼3% for both absorbing and scattering objects, as well as the small absorber in a scattering medium. Using this light source will require higher optical quality vessels than are currently provided by the manufacturer.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".