Technical Note: Detective quantum efficiency simulation of a‐Se imaging detectors using ARTEMIS
Bibliographic record
Abstract
PURPOSE: This work studies the detective quantum efficiency (DQE) of a-Se-based solid state x-ray detectors for medical imaging applications using ARTEMIS, a Monte Carlo simulation tool for modeling x-ray photon, electron and charged carrier transport in semiconductors with the presence of applied electric field. METHODS: ARTEMIS is used to model the signal formation process in a-Se. The simulation model includes x-ray photon and high-energy electron interactions, and detailed electron-hole pair transport with applied detector bias taking into account drift, diffusion, Coulomb interactions, recombination and trapping. For experimental validation, the DQE performance of prototype a-Se detectors is measured following IEC Testing Standard 62220-1-3. RESULTS: Comparison of simulated and experimental DQE results show reasonable agreement for RQA beam qualities. Experimental validation demonstrated within 5% percentage difference between simulation and experimental DQE results for spatial frequency above 0.25 cycles/mm using uniform applied electric field for RQA beam qualities (RQA5, RQA7 and RQA9). Results include two different prototype detectors with thicknesses of 240 μm and 1 mm. CONCLUSIONS: ARTEMIS can be used to model the DQE of a-Se detectors as a function of x-ray energy, detector thickness, and spatial frequency. The ARTEMIS model can be used to improve understanding of the physics of x-ray interactions in a-Se and in optimization studies for the development of novel medical imaging applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".