Low dead time digital SPAD readout architecture for realtime small animal PET
Bibliographic record
Abstract
CMOS integrated SPAD array design normally enforces a compromise between circuit functionalities and optical detection fill factor, never quite reaching the ideal detector configuration when both are integrated together on the same substrate. The emergence of vertical 3D integrated circuits (3DIC) changes this restriction and further adds in heterogeneous electronic integration, opening technological combinations otherwise difficult or impractical to obtain. Using this approach, a heterogeneous SPAD array detector prototype with digital readout for small animal PET was developed. It is based on Global Foundries 130 nm CMOS for digital and quenching circuits and on Teledyne Dalsa 0.8 μm HV CMOS process for the SPAD arrays. This paper focuses on the realtime digital architecture tailored for small animal PET scintillation detection, where in addition to tight timing and energy resolution, low PET dead time and high spatial resolution are required. A discriminator circuit is proposed to retain first-photon timing information while protecting against dark count rate triggering. The system provides two operational modes: a slower oscilloscope-like mode and a high rate PET mode providing 2.2M singles per second on a single 200 MHz LVDS transmitter. Performances in terms of electronic jitter and event detection are reported based on simulations.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".