ULTRA-Fast wiener filter based crystal identification algorithm applied to the LabPET<sup>TM</sup> phoswich detectors
Bibliographic record
Abstract
A Wiener filter based crystal identification (CI) algorithm achieving excellent discrimination accuracy (~98%) was recently proposed for identifying LYSO and LGSO crystals in phoswich detectors coupled to an avalanche photodiode. Such detectors are used in the LabPETtrade, an all-digital positron emission tomography (PET) scanner for small animal imaging recently developed in Sherbrooke. This algorithm was based on evaluating the scintillation decay constant and gain (a1and b0parameters, respectively) of events sampled at 45 MSPS and discriminating crystals by thresholding the a1spectra. The input gain was not considered in the CI process even if it must be computed. We propose a 2-fold faster CI approach which also takes into consideration the input gain coefficient of each crystal. The new algorithm incorporates the DAQ chain model -in the Z domain- to each individual crystal model evaluated in a Wiener filter calibration process. The identification is performed by evaluating a single parameter -compared to two parameters in previous Wiener CI algorithm- characterizing the percentage of each crystal gain contribution in the event signal. The CI algorithm demonstrated a discrimination rate accuracy >98.5% for LYSO-LGSO and >99% for LSO-GSO crystals in phoswich arrangement for 511 keV photopeak Although a calibration is required, initial implementation of the algorithm in a 400 MHz clocked FPGA can process up to 15 Mevents/sec.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".