FPGA/DSP-based coincidence unit and data acquisition system for the Sherbrooke animal PET scanner
Bibliographic record
Abstract
The use of field programmable gate arrays (FPGA) is a natural step in the evolution of modern PET scanners to improve system flexibility, increase design speed, and reduce electronic circuit size and cost. Hybrid high-performance programmable digital interfaces integrating an on-board FPGA and digital signal processor (DSP) were used to rebuild the coincidence units and data acquisition system of the Sherbrooke animal PET scanner. Logic was implemented within the FPGA to process singles and coincidence events, both trues and randoms. Coincidence validation is realized directly in the FPGA by an AND gate of the time signals generated by the scanner CF triggers. Twelve coincidence units from eight groups of detectors were equally divided between four FPGA/DSP modules to evenly distribute the event rate from the detectors. The event data is streamingly transmitted to the DSP for real-time histogramming into host memory or list mode data storage on disk. Event rates in excess of 10/sup 7//second can be achieved, which allows ultra-fast calibration and single-photon transmission measurements to be performed. The whole system occupies three full-length PCI slots in a standard PC computer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".