Towards a second-generation PET/MR insert with enhanced timing and count rate performance
Bibliographic record
Abstract
Previously we have developed a first-generation PET insert prototype for small animal PET/MR imaging, which used resistor-based charge division multiplexing circuits and SensL B-series silicon photomultipliers (SiPMs). In this work we present results from a second-generation readout board with improved timing and count rate performance. Three detector boards were tested: the first-generation readout board with SensL SPMArray4B (SiPM-B), the second-generation readout board with SensL ArrayC-30035-16P-PCB (SiPM-C) using the 'fast' outputs for timing, and the second generation board using Hamamatsu S11361-3050AE-04 MPPC arrays. Timing data were obtained with detector modules in coincidence with a single-pixel SensL MicroFJ-SMA-30035 reference detector and acquired using standard NIM electronics, while count rate data were acquired using the OpenPET data acquisition electronics system. The full-width at half-maximum (FWHM) coincidence time resolution (CTR) for the SiPM-B, SiPM-C and MPPC designs were 2600 ± 200 ps, 550 ± 50 ps, and 570 ± 30 ps, respectively. OpenPET waveform capture determined the mean signal durations, measured as time above 10% of the maximum amplitude, were 1850 ± 150 ns, 600 ± 25 ns, and 350 ± 25 ns, respectively, where the short signal of the MPPC resulted in reduced pileup effects at higher count rates. Decaying source measurements showed a non-paralyzable dead time of 1.30-1.41 µs for all three detectors tested, which was limited by the signal capture and processing time of the OpenPET system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".