Real Time Implementation of a Wiener Filter Based Crystal Identification Algorithm
Bibliographic record
Abstract
The recently launched LabPETtrade, a small animal Avalanche PhotoDiode (APD)-based PET scanner with quasi-individual readout and massively parallel processing, makes it possible to acquire real-time information necessary for Positron Emission Tomography (PET) image reconstruction. Since each APD is coupled to an LYSO/LGSO phoswich scintillator pair, an efficient crystal identification algorithm must be developed to sustain real-time crystal feature extraction in high PET count rate. Furthermore, a less application specific algorithm is needed to easily expand its use to a large range of crystal materials. For these reasons, a new ultra-fast crystal identification algorithm based on a Wiener filter is proposed. This optimum filter instantly recovers crystal parameters by minimizing a linear cost function. A one-dimension projection based discrimination is used to identify the scintillating crystal. The algorithm achieves a discrimination rate of for low-energy X-ray photons ( keV) and up to for high energy 511 keV photopeak photons, with a maximum throughput of 10 Mevents/sec when implemented in a field programmable gate array.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 0.001 |
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".