Seven general radiography x-ray detectors with pixel sizes ranging from 175 to 76 <i>μ</i> m: technical evaluation with the focus on orthopaedic imaging
Bibliographic record
Abstract
Abstract Aim . Flat panel detectors with small pixel sizes general can potentially improve imaging performance in radiography applications requiring fine detail resolution. This study evaluated the imaging performance of seven detectors, covering a wide range of pixel sizes, in the frame of orthopaedic applications. Material and methods . Pixel sizes ranged from 175 (detector A 175 ) to 76 μ m (detector G 76 ). Modulation transfer function (MTF) and detective quantum efficiency (DQE) were measured using International Electrotechnical Commission (IEC) RQA3 beam quality. Threshold contrast ( C T ) and a detectability index ( d ′) were measured at three air kerma/image levels. Rabbit shoulder images acquired at 60 kV, over five air kerma levels, were evaluated in a visual grading study for anatomical sharpness, image noise and overall diagnostic image quality by four radiologists. The detectors were compared to detector E 124 . Results . The 10% point of the MTF ranged from 3.21 to 4.80 mm −1 , in going from detector A 175 to detector G 76 . DQE(0.5 mm −1 ) measured at 2.38 μ Gy/image was 0.50 ± 0.05 for six detectors, but was higher for F 100 at 0.62. High frequency DQE was superior for the smaller pixel detectors, however C T for 0.25 mm discs correlated best with DQE(0.5 mm −1 ). Correlation between C T and the detectability model was good ( R 2 = 0.964). C T for 0.25 mm diameter discs was significantly higher for D 150 and F 100 compared to E 124 . The visual grading data revealed higher image quality ratings for detectors D 125 and F 100 compared to E 124 . An increase in air kerma was associated with improved perceived sharpness and overall quality score, independent of detector. Detectors B 150 , D 125 , F 100 and G 76 , performed well in specific tests, however only F 100 consistently outperformed the reference detector. Conclusion . Pixel size alone was not a reliable predictor of small detail detectability or even perceived sharpness in a visual grading analysis study.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".