Dark Count Impact for First Photon Discriminators for SPAD Digital Arrays in PET
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it