Modeling scintillator‐photodiodes as detectors for megavoltage CT
Bibliographic record
Abstract
The use of cadmium tungstate (CdWO4) and cesium iodide [CsI(Tl)] scintillation detectors is studied in megavoltage computed tomography (MVCT). A model describing the signal acquired from a scintillation detector has been developed which contains two steps: (1) the calculation of the energy deposited in the crystal due to MeV photons using the EGSnrc Monte Carlo code; and (2) the transport of the optical photons generated in the crystal voxels to photodiodes using the optical Monte Carlo code DETECT2000. The measured detector signals in single CdWO4 and CsI(Tl) scintillation crystals of base 0.275 x 0.8 cm2 and heights 0.4, 1, 1.2, 1.6 and 2 cm were, generally, in good agreement with the signals calculated with the model. A prototype detector array which contains 8 CdWO4 crystals, each 0.275 x 0.8 x 1 cm3, in contact with a 16-element array of photodiodes was built. The measured attenuation of a Cobalt-60 beam as a function of solid water thickness behaves linearly. The frequency dependent modulation transfer function [MTF(f)], noise power spectrum [NPS(f)], and detective quantum efficiency [DQE(f)] were measured for 1.25 MeV photons (in a Cobalt-60 beam). For 6 MV photons, only the MTF(f) was measured from a linear accelerator, where large pulse-to-pulse fluctuations in the output of the linear accelerator did not allow the measurement of the NPS(f). A two-step Monte Carlo simulation was used to model the detector's MTF(f), NPS(f) and DQE(f). The DQE(0) of the detector array was found to be 26% and 19% for 1.25 MeV and 6 MV photons, respectively. For 1.25 MeV photons, the maximum discrepancies between the measured and modeled MTF(f), relative NPS(f) and the DQE(f) were found to be 1.5%, 1.2%, and 1.9%, respectively. For the 6 MV beam, the maximum discrepancy between the modeled and the measured MTF(f) was found to be 2.5%. The modeling is sufficiently accurate for designing appropriate detectors for MVCT.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".