Spectral analysis of fundamental signal and noise performances in photoconductors for mammography
Bibliographic record
Abstract
PURPOSE: This study investigates the fundamental signal and noise performance limitations imposed by the stochastic nature of x-ray interactions in selected photoconductor materials, such as Si, a-Se, CdZnTe, HgI(2), PbI(2), PbO, and TlBr, for x-ray spectra typically used in mammography. METHODS: It is shown how Monte Carlo simulations can be combined with a cascaded model to determine the absorbed energy distribution for each combination of photoconductor and x-ray spectrum. The model is used to determine the quantum efficiency, mean energy absorption per interaction, Swank noise factor, secondary quantum noise, and zero-frequency detective quantum efficiency (DQE). RESULTS: The quantum efficiency of materials with higher atomic number and density demonstrates a larger dependence on convertor thickness than those with lower atomic number and density with the exception of a-Se. The mean deposited energy increases with increasing average energy of the incident x-ray spectrum. HgI(2), PbI(2), and CdZnTe demonstrate the largest increase in deposited energy with increasing mass loading and a-Se and Si the smallest. The best DQE performances are achieved with PbO and TlBr. For mass loading greater than 100 mg cm(-2), a-Se, HgI(2), and PbI(2) provide similar DQE values to PbO and TlBr. CONCLUSIONS: The quantum absorption efficiency, average deposited energy per interacting x-ray, Swank noise factor, and detective quantum efficiency are tabulated by means of graphs which may help with the design and selection of materials for photoconductor-based mammography detectors. Neglecting the electrical characteristics of photoconductor materials and taking into account only x-ray interactions, it is concluded that PbO shows the strongest signal-to-noise ratio performance of the materials investigated in this study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".