Optimization of solid tank design for fan-beam optical CT based 3D radiation dosimetry
Bibliographic record
Abstract
Abstract Optical computed tomography (CT) is one of the leading modalities for imaging gel dosimeters for 3D radiation dosimetry. There exist multiple scanner designs that have showcased excellent 3D dose verification capabilities of optical CT gel dosimetry. However, due to multiple experimental and reconstruction based factors there is currently no single scanner that has become a preferred standard. A significant challenge with setup and maintenance can be attributed to maintaining a large refractive index bath (1–15 l). In this work, a prototype solid ‘tank’ optical CT scanner is proposed that minimizes the volume of refractive index bath to between 10 and 35 ml. A ray-path simulator was created to optimize the design such that the solid tank geometry maximizes light collection across the detector array, maximizes the volume of the dosimeter scanned, and maximizes the collected signal dynamic range. An objective function was created to score possible geometries, and was optimized to find a local maximum geometry score from a set of possible design parameters. The design parameters optimized include the block length x bl , bore position x bc , fan-laser position x lp , lens block face semi-major axis length x ma , and the lens block face eccentricity x be . For the proposed design it was found that each of these parameters can have a significant effect on the signal collection efficacy within the scanner. Simulations scores are specific to the attenuation characteristics and refractive index of a simulated dosimeter. It was found that for a FlexyDos3D dosimeter, the ideal values for each of the five variables were: x bl = 314 mm, x bc = 6.5 mm, x lp = 50 mm, x ma = 66 mm, and x be = 0. In addition, a ClearView ™ dosimeter was found to have ideal values at: x bl = 204 mm, x bc = 13 mm, x lp = 58 mm, x ma = 69 mm, and x be = 0. The ray simulator can also be used for further design and testing of new, unique and purpose-built optical CT geometries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".