The role of x‐ray Swank factor in energy‐resolving photon‐counting imaging
Bibliographic record
Abstract
PURPOSE: Energy-resolved x-ray imaging has the potential to improve contrast-to-noise ratio by measuring the energy of each interacting photon and applying optimal weighting factors. The success of energy-resolving photon-counting (EPC) detectors relies on the ability of an x-ray detector to accurately measure the energy of each interacting photon. However, the escape of characteristic emissions and Compton scatter degrades spectral information. This article makes the theoretical connection between accuracy and imprecision in energy measurements with the x-ray Swank factor for a-Se, Si, CdZnTe, and HgI2-based detectors. METHODS: For a detector that implements adaptive binning to sum all elements in which x-ray energy is deposited for a single interaction, energy imprecision is shown to depend on the Swank factor for a large element with x rays incident at the center. The response function for each converter material is determined using Monte Carlo methods and used to determine energy accuracy, Swank factor, and relative energy imprecision in photon-energy measurements. RESULTS: For each material, at energies below the respective K edges, accuracy is close to unity and imprecision is only a few percent. Above the K-edge energies, characteristic emission results in a drop in accuracy and precision that depends on escape probability. In Si, and to some extent a-Se, Compton-scatter escape also degrades energy precision with increasing energy. The influence of converter thickness on energy accuracy and imprecision is modest for low-Z materials but becomes important when using high-Z materials at energies greater than the K-edge energies. CONCLUSIONS: Accuracy and precision in energy measurements by EPC detectors are determined largely by the energy-dependent x-ray Swank factor. Modest decreases in the Swank factor (5%-15%) result in large increases in relative imprecision (30%-40%).
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 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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".