A Fast Crystal Identification Algorithm Applied to the LabPET™ Phoswich Detectors
Bibliographic record
Abstract
Detectors based on LYSO and LGSO scintillators in a phoswich arrangement coupled to an avalanche photodiode are used in the LabPETtrade, an all-digital positron emission tomography (PET) scanner for small animal imaging developed in Sherbrooke. A Wiener filter based crystal identification (CI) algorithm achieving excellent discrimination accuracy was recently proposed for crystal identification of LYSO-LGSO phoswich detectors . This algorithm was based on estimating parameters describing the scintillation decay time constant and the light yield of events sampled at 45 MSPS. The CI process was performed by applying a threshold on the scintillation decay parameter of events. The light yield was not considered in the CI process even if it should be. We propose a 2-fold faster CI approach which takes both the scintillation decay and light yield coefficients of each crystal into consideration. The new algorithm uses the previous Wiener filter based algorithm as a calibration process in order to evaluate the model of each individual crystal. The DAQ chain model as a priori knowledge is then incorporated into the model of each crystal and the output signal is estimated. The CI is performed by evaluating a single parameter representing the percentage contribution of each crystal characteristics in the event signal. The CI algorithm demonstrated a discrimination rate accuracy for LYSO-LGSO LabPET detectors and for LSO-GSO crystals in phoswich arrangement for 511 keV events. Although a calibration is required, the real-time implementation of the new CI algorithm needs 2 times less direct operations. An FPGA clocked at 400 MHz can process up to 25 M events/sec with such an algorithm.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".