LabPET pulse simulator for crystal identification validation of multi-layer phoswich detectors
Bibliographic record
Abstract
A detector pulse simulator was recently developed to simulate digital output signals from LabPET, an APD-based small animal Positron Emission Tomography (PET) scanner developed at Universite¿ de Sherbrooke. The main goal of the simulator was to validate the digital crystal identification (CI) and timing algorithms implemented in the LabPET. The pulse simulator uses information generated by Monte Carlo simulation, such as energy, time of arrival and position of interactions in a LYSO-LGSO (80%Gd) phoswich detector to generate digital output signals in which Poisson photon statistics and electronic noise are included. We report here the validation of the Wiener filter CI results for new types of fast and high luminosity scintillation crystals that can potentially be used as new depth of interaction phoswich detectors. The fast Wiener filter-based CI method was investigated for discriminating two and three layer detectors using GSO, and new LSO: Ce,Ca and LGSO(10%Gd) crystals with decay time differences as close as 13 ns. CI error rate was 0.5%-3% for various two layer phoswich detectors and 6% for a three-layer detector for energies higher than 100 keV.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".