An experimentally validated simulation model of a two-bin flat-panel cadmium telluride photon-counting detector for spectroscopic breast imaging applications
Bibliographic record
Abstract
Flat-panel, cadmium telluride (CdTe) photon counting detectors (PCDs) are of interest for contrast-enhanced spectral mammography (CESM). Modelling of the frequency-dependent imaging performance of CdTe PCDs is useful for optimization of technique parameters. The purpose of this work is to experimentally validate a Monte-Carlo-based model of the frequency-dependent imaging performance of a CdTe PCD for contrast-enhanced breast imaging. Our Monte-Carlo model accounts for the basic physics of x-ray detection by PCDs, including the finite range of photo-electrons, characteristic emission and subsequent reabsorption, the finite size of charge clouds due to diffusion and Coulomb repulsion, electronic noise and energy thresholding. We validated the model using a two-bin CdTe PCD with charge summation for charge-sharing correction. The detector consists of a 750-um-thick CdTe converter with 100 um pixels. Our model was used to predict both the modulation transfer function (MTF) and the normalized noise power spectrum (NNPS) of both energy bins, in addition to the cross NPS between energy bins. Validation was performed for 40 kVp, 45 kVp and 50 kVp tube voltages. For each tube voltage, we validated for energy thresholds that resulted in photon ratios between the two energy bins of 1:2, 1:1 and 2:1. In all cases, our model predicted the MTF of both energy bins within 3%. The modelled NNPS was 5% to 15% of the measured NNPS. We conclude that the Monte-Carlo-based model can be used to model the frequency-dependent signal and noise properties of contrast-enhanced spectral mammography implemented with CdTe PCDs.
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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 | 0.000 |
| 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 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".