A novel DOI detector design with high encoding ratio for PET applications
Bibliographic record
Abstract
We designed a novel detector module combining the dual-ended readout design with the traditional block detector for resolving depth of interaction (DOI) and enabling high encoding ratio (number of scintillation crystals per photodetector) and high photon collection efficiency. The design and performance of the dual-ended readout block detector were investigated using Monte-Carlo simulation. Detector modules of varying encoding ratios were modeled and photons were generated and tracked within the module to determine the geometric X, Y (crystal identification), DOI resolutions and collection efficiency of the system. The X and Y resolutions were expressed as the error in identifying the interacting scintillation crystal and the resolutions were optimized for each encoding ratio by adjusting the thickness of the optical light guide. Results showed that a detector module with an encoding ratio up to 3.125 with 2 mm light guides can achieve minimal error in identifying the interacting scintillation crystal,; 65% collection efficiency. This design allows for a 6.25 fold reduction in the number of channels compared to a one-to-one dual-ended readout configuration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".