Simulation of scintillation light output in LYSO scintillators through a full factorial design
Bibliographic record
Abstract
Individually coupled scintillation detectors used in positron emission tomography (PET) imaging suffer from important signal losses due to the suboptimal light collection from crystals. As only a fraction of the light is generally extracted from long and thin scintillators, it is important to identify and understand the predominant causes of signal loss in order to eventually recover it. This simulation study investigates the multiple factors affecting the light transport in high-aspect ratio LYSO scintillators wrapped in specular reflectors through a full factorial design. By exploring various combinations of crystal geometry, readout conditions and wrapping conditions, it was found that an optimum light output can only be achieved through a careful selection of highly reflective material along with high-transmittance optical adhesive used to bond the reflector. Decreasing the adhesive thickness was also found to have a positive outcome in most explored configurations, however to a much lesser extent. Suboptimal reflectivity and adhesive transmittance also lead to an asymmetric light output distribution dependent on the depth of interaction of the radiation, potentially degrading energy resolution. By identifying the factors causing the most significant scintillation light losses through a factorial design, the most promising detector configurations have been identified in the quest for optimal light collection from scintillators.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".