TU‐FF‐A3‐01: X‐Ray and Optical Monte Carlo Study of Thick, Segmented Scintillators for MV Imaging
Bibliographic record
Abstract
Purpose: To study the imaging characteristics of thick, segmented, 2‐D CdWO4 crystal‐photodiode detectors as a function of crystal height, septa material and optical reflectivity, x‐ray beam spectrum and beam divergence using a two‐step Monte Carlo approach involving both x‐ray photon transport at megavoltage (MV) energies and the optical photon transport in scintillator and photodiodes. Method and Materials: We have studied the spatial frequency dependent detective quantum efficiency (DQE) of thick, segmented, 2‐D CdWO4 crystals in contact with silicon photodiode arrays. The energy deposited into the 3‐D voxels (1 × 1 × 1 mm3, septa thickness = 0.15 mm, fill factor = 72%) of the detector for each of the 6 and 3.5 MV x‐ray photons in a normally incident pencil beam was calculated using the DOSXYZnrc user code of the EGSnrc Monte Carol system. The isotropically emitted optical photons in each voxel were calculated using the average CdWO4 optical yield and transported to the photodiode array using DETECT2000 optical Monte Carlo code. A 10° beam divergence angle was also simulated. The detector DQE was calculated using the spatial distribution of optical photons. Results: The DQE increases with the crystal height only if the reflectivity of the septa material is high (0.975). For poor reflectivity (0.65 and 0.8), the increase in the DQE of the taller crystals to MV photons is seriously offset (from 42% to less than 20% for 3 cm tall crystals) by the decreased probability of detecting optical photons. Similarly, the increase in DQE due to the lower energy photons is obtained if the high reflectivity of septa material is maintained for the detector. Beam divergence in thick crystals also reduces the DQE. Conclusion: High reflectivity of the septa in thick, segmented scintillation detectors is very important to achieve high DQE.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".