Performance Characterization of MPPC Modules for TOF-PET Applications
Bibliographic record
Abstract
PET detectors commonly feature large numbers of multipixel photon counters (MPPCs), requiring data acquisition systems with significant signal multiplexing or using application specific integrated circuits (ASICs) for independent MPPC readout. We evaluate here two Hamamatsu C13500-4075LC-12 detector modules designed for time-of-flight (TOF) PET. Each module has a 12 × 12 lutetium fine silicate crystal array (4.2 χ 4.2 × 20 mm3) one-to-one coupled to a 12 × 12 array of 4 mm MPPCs. Each detector has 8 18-channel ASICs utilizing time-over-threshold (ToT) readout. At low event rates, the detectors have full-width at half-maximum (FWHM) energy resolution of 11.8%±0.4%, measured in linearized energy spectra, and 275 ps FWHM coincidence timing resolution (CTR) using energy window of ±1 photopeak FWHM. The 511 keV photopeak amplitude in the ToT spectrum changed 0.24%/°C. There was <;10% event loss up to 1450 kcps/detector. From count rates of 35-1450 kcps the 511 keV photopeak position varied by 1.2%, while photopeak FWHM in the ToT spectra increased by 54%, from 5.8% to 9.0%, corresponding to energy resolution changing from 11.8% to approximately 18.0%. Over this count rate range the CTR varied from 285 to 435 ps FWHM. The TOFPET module performance suggests they are well suited to use in whole-body PET applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".