The detective quantum efficiency of cadmium telluride photon‐counting x‐ray detectors in breast imaging applications
Bibliographic record
Abstract
Abstract Purpose In breast imaging applications, cadmium telluride (CdTe) photon counting x‐ray detectors (PCDs) may reduce radiation dose and enable single‐shot multi‐energy x‐ray imaging. The purpose of this work is to determine the upper limits of the detective quantum efficiency (DQE) of CdTe PCDs for x‐ray mammography and to compare them with the published DQEs of energy‐integrating detectors (EIDs) and other PCDs. Methods We calibrated and validated a Monte Carlo (MC) model of the DQE of CdTe PCDs using an XCounter CdTe PCD. Our model accounted for charge sharing, electronic noise, and charge summation logic. We used a 28 kVp Mo/Mo spectrum hardened by 3.9 cm of Lucite to optimize the detector thickness and energy threshold for pixel sizes of 50, 85, and 100 m with and without inter‐pixel charge summation logic. The figure of merit used for optimization was the integral of the DQE, which is equivalent to the detectability index for a delta function task function, which represents a high‐frequency task. Results For an electronic noise level equal to that of the XCounter, the optimal DQE(0) without charge summing was 0.74. Charge summing for charge‐sharing correction reduced DQE(0) by 14% due to an increase in electronic noise. Reducing the electronic noise to ∼0.5 keV per pixel in combination with charge summing resulted in DQE(0) 0.78 for 85 m pixels, which is approximately equal to that of a ‐Se and slot‐scanning silicon‐strip PCDs. At higher spatial frequencies, and for matched pixel sizes, the DQE was inferior to that of a ‐Se EIDs and superior to that of slot‐scanning silicon‐strip PCDs in the scan direction but inferior in the slit direction. Conclusions (1) CdTe PCDs have the potential to provide a zero‐frequency DQE equal to that of a ‐Se EIDs and slot‐scanning silicon‐strip PCDs, but this will require electronic noise levels ∼0.5 keV per pixel. (2) At mid‐to‐high spatial frequencies the DQE of CdTe PCDs may be (a) inferior to that of a ‐Se EIDs and slot‐scanning silicon‐strip PCDs in the slit direction, and (b) superior to slot‐scanning silicon‐strip PCDs in the scan direction.
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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".