Use of Noise Power Spectra (NPS) for quality control in digital radiography
Bibliographic record
Abstract
Quality control (QC) guidance documents recommend various tests for evaluation of different parameters of x ray imaging systems’ performance. QC tests can be time consuming, user-dependent and require specialized tools. The aim of this thesis is to investigate the noise power spectrum (NPS) as a QC constancy test which is simple, fast and lends itself easily to automated analysis. Uniform images were acquired under different conditions representing deviations from ideal performance using two digital x-ray systems. The stationarity and ergodicity of the noise was assessed. The normalized NPS (NNPS) were calculated using the methodology of the international electrotechnical commission. The total relative difference was used to quantify the changes in the NNPS. The NNPS was computed for images: with focal spot blooming, collected using large and small focal spot (to mimic resolution change), various tube voltage values, with and without defective pixels, with residual image and with a mismatched anti-scatter grid. Results showed that the NPS method is not sensitive to image lag and focal spot blooming investigated in this study. However, the NPS method was sensitive to changes in resolution introduced by changing the focal spot size, kV deviations as small as 1 kV, defective pixels representing 0.01% of the image pixel and 0.98 MSE difference from the original image, affixed pattern artifacts and a mismatched grid. The NPS was decomposed into its components (fixed pattern, quantum and electronic) to investigate the effect of different performance deviations on the NPS components. The negligibility of the electronic noise was verified. The results showed the fixed pattern changes impacted the fixed pattern NPS component the most and the changes associated with quantum noise affected the quantum component. This thesis suggests the NPS is sensitive to a variety of deviations in system parameters and performance metrics likely to arise in the quality control of digital radiography systems. NPS decomposition can further help identify the source of deviations. The NPS has the potential to be used as a constancy test for routine quality control of DR systems.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".