Commissioning of a solid tank design for fan-beam optical CT based 3D radiation dosimetry
Bibliographic record
Abstract
Abstract Objective. Optical computed tomography (CT) is one of the leading modalities for imaging gel dosimeters used in the verification of complex radiotherapy treatments. In previous work, a novel fan-beam optical CT scanner design was proposed that could significantly reduce the volume of the refractive index baths that are commonly found in optical CT systems. Here, the proposed scanner has been manufactured and commissioned. Approach. Image reconstruction is performed through algebraic reconstruction technique and iterated using the fast iterative shrinkage-thresholding algorithm (FISTA) algorithm. Ray tracing for algebraic reconstruction was performed using an in-house developed ray tracing simulator. A set of Sylgard® 184 phantoms were created to commission spatial resolution, geometric deformity, contrast-to-noise ratio (CNR), and scan settings. Main Results. The scanner is capable of a 0.929 mm −1 spatial resolution, observed at 200 iterations, although the spatial resolution is highly dependent on the number of iterations. The geometric distortion, measured by scanning a needle phantom with the prototype scanner as well as a conventional x-ray CT was found to be within <0.25 mm. The CNR was found to peak between 65 and 190 occurring between 50 and 100 iterations and was highly dependent on the region chosen for background noise calculation. The proposed scanner is capable of scanning and reading out slices in less than 1 min per slice. Significance. This work displays the viability of a fan-beam optical CT scanner with minimal index matching using ray-traced algebraic reconstruction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".