The detective quantum efficiency of photon‐counting x‐ray detectors using cascaded‐systems analyses
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Single-photon counting (SPC) x-ray imaging has the potential to improve image quality and enable new advanced energy-dependent methods. The purpose of this study is to extend cascaded-systems analyses (CSA) to the description of image quality and the detective quantum efficiency (DQE) of SPC systems. METHODS: Point-process theory is used to develop a method of propagating the mean signal and Wiener noise-power spectrum through a thresholding stage (required to identify x-ray interaction events). The new transfer relationships are used to describe the zero-frequency DQE of a hypothetical SPC detector including the effects of stochastic conversion of incident photons to secondary quanta, secondary quantum sinks, additive noise, and threshold level. Theoretical results are compared with Monte Carlo calculations assuming the same detector model. RESULTS: Under certain conditions, the CSA approach can be applied to SPC systems with the additional requirement of propagating the probability density function describing the total number of image-forming quanta through each stage of a cascaded model. Theoretical results including DQE show excellent agreement with Monte Carlo calculations under all conditions considered. CONCLUSIONS: Application of the CSA method shows that false counts due to additive electronic noise results in both a nonlinear image signal and increased image noise. There is a window of allowable threshold values to achieve a high DQE that depends on conversion gain, secondary quantum sinks, and additive noise.
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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 | 0.000 |
| 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 | 0.000 | 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 it