Detective quantum efficiency: a standard test to ensure optimal detector performance and low patient exposures
Bibliographic record
Abstract
The detective quantum efficiency (DQE), expressed as a function of spatial frequency, describes the ability of an x-ray detector to produce high signal-to-noise ratio (SNR) images. While regulatory and scientific communities have used the DQE as a primary metric for optimizing detector design, the DQE is rarely used by end users to ensure high system performance is maintained. Of concern is that image quality varies across different systems for the same exposures with no current measures available to describe system performance. Therefore, here we conducted an initial DQE measurement survey of clinical x-ray systems using a DQE-testing instrument to identify their range of performance. Following laboratory validation, experiments revealed that the DQE of five different systems under the same exposure level (8.0 μGy) ranged from 0.36 to 0.75 at low spatial frequencies, and 0.02 to 0.4 at high spatial frequencies (3.5 cycles/mm). Furthermore, the DQE dropped substantially with decreasing detector exposure by a factor of up to 1.5x in the lowest spatial frequency, and a factor of 10x at 3.5 cycles/mm due to the effect of detector readout noise. It is concluded that DQE specifications in purchasing decisions, combined with periodic DQE testing, are important factors to ensure patients receive the health benefits of high-quality images for low x-ray exposures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".