Simulation of signal losses in highly pixelated scintillator arrays read out by discrete photodetectors
Bibliographic record
Abstract
The performance of scintillation detectors used in Positron Emission Tomography imaging strongly depends on the scintillation light transport from the crystal to the photodetector. In highly pixelated scintillator arrays with individual pixels approaching millimetric cross section, the loss of signal is compounded with crosstalk effects, squandering valuable signal to adjacent pixels, and with light absorption in lateral faces adhesive materials and imperfect reflectors. The purpose of this simulation study is to uncover processes responsible for light losses in scintillator arrays. Four sources of losses through crosstalk between pixels were identified, namely 1) escaping photoelectrons to other pixels after photoelectric interactions, 2) X-ray fluorescence and Auger emission, 3) reflector transparency to scintillation light, and 4) light leakage to other crystals due to adhesive material between reflectors and scintillators in which optical photons can propagate to other crystals. An important source of signal loss and energy resolution degradation was found to be related to the transmittance of the adhesive material used to bond reflectors to scintillators. Moreover, the angular distribution of scintillation photons impinging on the detection face was assessed in order to weigh the proportion of trapped photons through total internal reflection due to the refractive index difference between scintillators and optical coupling medium.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".