A pulse simulator for crystal identification validation of phoswich detectors used in positron emission tomography
Bibliographic record
Abstract
Crystal identification (CI) of phoswich detectors is a technique used in positron emission tomography (PET) for improving spatial resolution through depth-of-interaction determination or higher pixelization. Digital algorithms using advanced digital signal processing techniques currently provide the most powerful approaches for CI of phoswich detectors made of crystals with only slightly different scintillation decay times. Such methods can be implemented in the all-digital architecture of LabPET, a small animal PET scanner developed in Sherbrooke, for fast and accurate real-time CI. In order to validate the new CI algorithms and assess their performance for different front-end electronics, a pulse generator simulator was developed to generate PET signals and investigate the effects of factors such as electronic noise, photon statistics and pulse shaping filter. The pulse generator was validated with LabPET-like pulses and CI results were compared with experimental data. The pulse simulator enables CI algorithms to be validated together with detector performance such as energy and timing resolution at an early stage of scanner design.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".