Fundamental x-ray interaction limits in diagnostic imaging detectors: Frequency-dependent Swank noise
Bibliographic record
Abstract
A frequency-dependent x-ray Swank factor based on the "x-ray interaction" modulation transfer function and normalized noise power spectrum is determined from a Monte Carlo analysis. This factor was calculated in four converter materials: amorphous silicon (a-Si), amorphous selenium (a-Se), cesium iodide (CsI), and lead iodide (PbI2) for incident photon energies between 10 and 150 keV and various converter thicknesses. When scaled by the quantum efficiency, the x-ray Swank factor describes the best possible detective quantum efficiency (DQE) a detector can have. As such, this x-ray interaction DQE provides a target performance benchmark. It is expressed as a function of (Fourier-based) spatial frequency and takes into consideration signal and noise correlations introduced by reabsorption of Compton scatter and photoelectric characteristic emissions. It is shown that the x-ray Swank factor is largely insensitive to converter thickness for quantum efficiency values greater than 0.5. Thus, while most of the tabulated values correspond to thick converters with a quantum efficiency of 0.99, they are appropriate to use for many detectors in current use. A simple expression for the x-ray interaction DQE of digital detectors (including noise aliasing) is derived in terms of the quantum efficiency, x-ray Swank factor, detector element size, and fill factor. Good agreement is shown with DQE curves published by other investigators for each converter material, and the conditions required to achieve this ideal performance are discussed. For high-resolution imaging applications, the x-ray Swank factor indicates: (i) a-Si should only be used at low-energy (e.g., mammography); (ii) a-Se has the most promise for any application below 100 keV; and (iii) while quantum efficiency may be increased at energies just above the K edge in CsI and PbI2, this benefit is offset by a substantial drop in the x-ray Swank factor, particularly at high spatial frequencies.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".