Scatter levels in triple-energy photon-counting x-ray imaging
Bibliographic record
Abstract
Triple-energy imaging with photon-counting x-ray detectors may enable real-time background subtraction for interventional radiography. X-ray scatter is expected to affect the robustness of image generation and the resulting image quality. The purpose of this work was to measure scatter levels as a function of the energy bin for triple-energy imaging with photon-counting x-ray detectors. We used the air-gap method to measure the scatter levels for a 20cm, 15cm, and 10cm-thick PMMA phantom. The phantom was imaged using a cadmium telluride photon-counting detector with two energy bins and analog charge summing for charge-sharing suppression. Triple-energy data sets comprised of a low-energy bin, medium-energy bin and high-energy bin were generated from two separate dual-energy data sets for tube voltages of 60kV, 80kV and 100kV. The energy thresholds were chosen to optimize the contrast-to-noise ratio. The scatter-to-primary ratio (SPR), primary fraction, and scatter fraction were measured for fields of view ranging from 5×5cm2 to 26×26cm2 at the detector plane. The resulting total SPRs (i.e. total primary/total scatter) ranged from 0.2 to 2.5 across all tube voltages and fields of view. For a fixed tube voltage, the SPR varied by a factor of 2.5 across the energy bins, with the lowest energy bin having the highest SPR and the highest energy bin having the lowest SPR. In all cases, for a fixed tube voltage, the SPR of a particular energy bin was a constant fraction of the total SPR. For example, the SPR in the low-energy bin was typically ~1.3 to 1.5 times that of the total SPR. The SPR of the high-energy bin was ~0.6 to 0.7 times that of the total SPR. The results of this study will be critical to understanding image generation and image quality in triple-energy photon-counting imaging of iodine for angiographic applications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".