A PET camera simulator with multispectral acquisition capabilities
Bibliographic record
Abstract
The Sherbrooke PET (positron emission tomography) simulator was designed and built to investigate the performance characteristics of a high-resolution PET camera based on avalanche photodiode detectors. The simulator consists of a computer-controlled scanning table with 32 detection channels shared between front-end casettes and FASTBUS boards, and of a PC-based multichannel analyzer (MCA) used as histogramming memory for multiparametric data acquisition. Tomograph data are collected by scanning one of two opposite arrays of detectors and by rotating the object in a predetermined sequence to simulate a complete ring of detectors with various sampling schemes. All acquisition parameters are programmable through digital-to-analog converters or on-board registers. Data can be acquired in several modes: calibration, where direct or coincident energy spectra from all detectors can be registered simultaneously; standard, where only energy-validated coincident events are histogrammed as lines-of-response (LOR) addresses; and multispectral, where the LOR address is encoded with the energy information to provide a multiparameter histogram.>
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.009 | 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".