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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".