Dark Count Impact for First Photon Discriminators for SPAD Digital Arrays in PET
Bibliographic record
Abstract
To increase contrast in positron emission tomography (PET) images, researchers are investigating detectors that reach below the nanosecond timing resolution. This allows a tight coincidence window which reduces random coincidence counts in the acquired data, as well as to include time-of-flight information into the reconstruction algorithms. With this goal in mind, single photon avalanche diode (SPAD) arrays have been under study for their excellent timing performances. However, their spurious dark counts can blur the start of PET signals where timing information is the most precise and create false starts in the acquisition system, introducing dead time. To minimize these problems in digital SPAD systems using a single time to digital converter (TDC) per PET channel, dark count discriminator circuits are required to reduce timing errors and increase the triggering efficiency in presence of dark counts. This paper compares the performance of a probabilistic and a novel delay line based dark count discriminator. Simulations of a SPAD array investigate the impact of dark counts on triggering efficiency and coincidence timing. Results show that the probabilistic discriminator provides excellent event recovery with regard to dark counts at the cost of some coincidence timing resolution. On the other hand, the delay line discriminator maintains the peak coincidence timing resolution but does not provide as much efficiency at high dark count rate levels.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".