Performance characterization of a dual-threshold time-over-threshold APD-based detector front-end module for PET imaging
Bibliographic record
Abstract
The LabPET II front-end module is an avalanche photodiode (APD) based pixelated detector designed to achieve submillimetric spatial resolution in pre-clinical Positron Emission Tomography (PET). This technology is also based on Time-over-Threshold (ToT) signal processing and designed to be used as a generic platform for ultra-high resolution PET imaging of small and medium-size animals. The basic building block uses a 4×8 array of 1.12×1.12×12 mm3Lu1.9Y0.1SiO5:Ce (LYSO) scintillator pixels with one-to-one coupling to a 4×8 pixelated APD array mounted on a ceramic carrier. Four of these detectors are mounted on a PCB with two 64-channel ASICs interfacing to two detector modules each. Signals from each APD pixel can be individually processed by dedicated dual-threshold ToT channels providing timing and energy data. Energy calibration was performed using gamma ray sources in the range 300–1275 keV to correct the non-linearity of the ToT signal and obtain energy spectra. Energy and timing performance of the complete front-end module was evaluated. Results confirm the functionality of the dual threshold ToT circuit implemented in the 64-channel ASIC, as well as the physical performance of the most recent LabPET II version of APD-based detectors for applications in high-resolution PET imaging.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".