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 5×5cm<sup>2</sup> to 26×26cm<sup>2</sup> 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 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 | 0.000 |
| 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".