The effects of the spatio-energetic system response of cadmium telluride x-ray detectors on basis-material decomposition for iodine imaging tasks
Bibliographic record
Abstract
Spectroscopic x-ray detectors are under development in academic and industry laboratories, and have been receiving attention for their ability to perform single-shot, multi-material decomposition. However, a number of physical processes, including charge sharing and characteristic emission, cause spectral distortion which results in image degradation. In this study, the effects of the system response of cadmium telluride x-ray detectors on basis-material decomposition were investigated. A spatio-energetic model of the system response of spectroscopic x-ray detectors was developed and incorporated into material decomposition of simulated flat-field images. The spatio-energetic decomposition was compared against two other decomposition methods: one which incorporated the energy response and one which assumed an ideal system response. The results were also compared against the decomposition of images simulated assuming ideal conditions (i.e. no charge sharing or characteristic emission). All decomposition methods investigated here were shown to preserve linearity of the iodine signal with respect to the background. However, inclusion of the non-ideal system response resulted in a 3-fold reduction in SNR. Investigating the effects of the spatio-energetic system response on the spatial resolution of basis-material images will be a focus of future work.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".