Software-based charge sharing correction for spectroscopic x-ray detectors
Bibliographic record
Abstract
Spectroscopic x-ray detectors, which are currently under development, may have the potential to revolutionize diagnostic x-ray imaging through their ability to perform single-shot, multi-energy imaging. However, the sharing of charge between neighbouring detector elements results in degraded image quality. The goal of this project was to develop a computer algorithm to correct for the effects of charge sharing. The 3 primary objectives were: (1) to develop a model of the system response of spectroscopic x-ray detectors which accounts for the spatio-energetic effects of charge sharing, (2) to develop a charge sharing correction algorithm, based on the maximum likelihood expectation-maximization (MLEM) algorithm which incorporates the spatio-energetic system response model and (3) to assess the accuracy of the charge sharing correction algorithm by applying it to simulated x-ray images. The MLEM algorithm was able to correct for the spectral distortion resulting from charge sharing with reasonable accuracy. Its performance was somewhat erratic due to the non-linearity of the algorithm, but was found to perform better when applied high-energy x-ray spectra.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".