Signal Deconvolution Concept Combined With Cubic Spline Interpolation to Improve Timing With Phoswich PET Detectors
Bibliographic record
Abstract
PET imaging scanners based on all-digital architecture offer greater data processing flexibility and the possibility to recalibrate the system with simple software procedures. The LabPET scanner, designed at the Universite de Sherbrooke, is one such device. It is built around dual LYSO/LGSO scintillators in a phoswich arrangement coupled to Avalanche Photodiodes (APD) and combined with highly parallel readout and processing electronics. This approach enables the implementation of advanced real time digital signal processing methods to compute energy resolution, crystal identification and the arrival time of events. Timing extraction represents the highest challenge because of the low sampling frequency (45 MHz), the quantization error and the presence of a Zero Order Hold (ZOH) in the system. The aim of this paper is to present a method to increase coincidence time accuracy by adequately addressing each of these limitations. The proposed method uses a Deconvolution concept based on adaptive filter theory preceded by Cubic Spline interpolation to improve digital timing performance. The method achieves 4.5 ns, 8.2 ns, and 6.5 ns timing resolution with LYSO-LYSO, LGSO-LGSO and mixed crystals phoswich coincidences, respectively.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".