Real-Time Coincidence Detection System for Digital High Resolution APD-based Animal PET Scanner
Bibliographic record
Abstract
A centralized, fully digital, FPGA-based coincidence detection system has been developed for the LabPET APD-based scanner. The digital flexibility allows excellent timing resolution using digital signal processing and high precision crystal identification. In this digital architecture, fast AND-gate coincidence detection is no longer possible due to signal analysis delay. Timestamp based coincidences must be carried out by a central digital process that handles huge amounts of data. A 45 MHz system clock is used by free running ADCs and reference time counters. Event timestamp is refined to ~0.7 ns resolution with digital analysis. A real-time digital coincidence detection system capable of processing 32 million single events per second is proposed to support a fully digital APD-based architecture. The coincidence engine retains a technology independent structure, making it easily reusable in subsequent generation architectures. The system detects prompt coincidences and evaluates random coincidences using both a delayed-window coincidence and the singles count rate. Finally, it supports dynamic adaptive coincidence windowing for multi-crystal PET scanners, ranging from 0 to 100 ns in 0.7 ns increments
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".