A bench‐top megavoltage fan‐beam CT using ‐photodiode detectors. I. System description and detector characterization
Bibliographic record
Abstract
We describe the components of a bench-top megavoltage computed tomography (MVCT) scanner that uses an 80-element detector array consisting of CdWO4 scintillators coupled to photodiodes. Each CdWO4 crystal is 2.75 x 8 x 10 mm3. The detailed design of the detector array, timing control, and multiplexer are presented. The detectors show a linear response to dose (dose rate was varied by changing the source to detector distance) with a correlation coefficient (R2) nearly unity with the standard deviation of signal at each dose being less than 0.25%. The attenuation of a 6 MV beam by solid water measured by this detector array indicates a small, yet significant spectral hardening that needs to be corrected before image reconstruction. The presampled modulation transfer function is strongly affected by the detector's large pitch and a large improvement can be obtained by reducing the detector pitch. The measured detective quantum efficiency at zero spatial frequency is 18.8% for 6 MV photons which will reduce the dose to the patient in MVCT applications. The detector shows a less than a 2% reduction in response for a dose of 24.5 Gy accumulated in 2 h; however, the lost response is recovered on the following day. A complete recovery can be assumed within the experimental uncertainty (standard deviation <0.5%); however, any smaller permanent damage could not be assessed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".