Effect of inter-crystal scatter events on coincidence detection in LabPET scanners
Bibliographic record
Abstract
Compton scattering of gamma photons can give rise to inter-crystal crosstalk in detector arrays for positron emission tomography (PET). PET systems made of pixelated detectors with individual readout can recover inter-crystal scatter (ICS) events in the list-mode data file, but these events are generally ignored since their inclusion in the image reconstruction process leads to undesirable image noise. For such systems, including lines-of-response (LORs) resulting from triple events increases detection efficiency, but at the expense of degrading image quality. Therefore, it is important to understand the various processes contributing to triple coincidences to underline neglected sources of noise counteracting the benefit of ICS inclusion. It was found that a main source of mispositioned LORs is caused by triple randoms consisting of a true coincidence with a single detection. Limiting the contribution of these triple randoms when recovering ICS events resulted in improvements of image quality.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".