Timing improvement by low-pass filtering and linear interpolation for the LabPET<sup>TM</sup> scanner
Bibliographic record
Abstract
Digital processing for positron emission tomography (PET) scanners commonly relies on low frequency sampling (≪65 MHz) for reducing power consumption. Timestamps must then be interpolated between samples to achieve adequate time resolution for coincidence detection of annihilation radiation. A low-pass filter based interpolation algorithm adding up to 31 samples between original samples was designed to improve both the energy and timing resolution of the LabPETTMscanner. An energy resolution refinement of ˜2 bits can be achieved with such a technique. The better estimation of triggering threshold leads to a more accurate timestamp generation. Timestamp accuracy was investigated as a function of trigger level (5-50% of maximum value). With the trigger threshold set at 20%, coincidence time resolution of ˜5.0 ns for LYSO-LYSO and ˜9.6 ns for LGSO-LGSO are obtained. A real time implementation of the algorithm was achieved in a Xilinx FPGA.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".