System Integration of the LabPET Small Animal PET Scanner
Bibliographic record
Abstract
To address modern molecular imaging requirements, a digital positron emission tomography scanner for small animals has been developed at Universite de Sherbrooke. Based on individual readout of avalanche photodiodes (APD) coupled to a LYSO/LGSO phoswich array, the scanner supports up to 3072 channels in a 16.2 cm diameter, 7.5 cm axial field of view with an isotropic 1.2 mm FWHM intrinsic spatial resolution at the center of the FOV. Custom data acquisition boards sample APD signals at 45 MHz and compute in real time crystal identification, energy and timing information of detected events at rates of up to 1250 raw counts per second per mm2(10k cps/channel). Real time digital signal analysis also filters out events outside the photopeak with crystal granularity to eliminate Compton events and electronic noise. Retained events are then merged into a single stream through a real-time sorting tree, at which end the prompt and delayed coincidences are extracted. A single Firewire link handles both control and data transfers with a computer. The LabPETtrade features four data recording modes, giving the user the choice to retain data for research or to minimize file size for high coincidence count rate and imaging purposes. The electronic system also supports time synchronized data insertion for flags such as vital signs used in gated image reconstruction. Aside from data acquisition, hardware can generate live energy and discrimination histograms suitable for fast, automatic channel calibration.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.030 | 0.011 |
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".