Calibration process for improving Crystal Identification rate in the LabPET™ phoswich detectors
Bibliographic record
Abstract
The LabPET™ is a small animal APD-based PET scanner using LYSO-LGSO phoswich detectors. A digital Crystal Identification (CI) method based on a Wiener filter, chosen for its low computation burden and low error rate, was implemented to localize the position of interaction in the phoswich. To facilitate its implementation, a unique Data AcQuisition (DAQ) model, in the Z-domain, was applied to every channel in the scanner, resulting in uneven CI error rates due to slight variations in detector channel characteristics. To improve the overall scanner identification performance, we propose to compute personalized DAQ model reference for every individual channel. Four individual DAQ models based on different minimization of cost functions were investigated. The first DAQ model uses a standard Wiener filter. The second DAQ model maximizes similarity to DAQ behavior. The third DAQ model attempts to minimize crystal decay time bias relative to the reference values and finally, the fourth DAQ model maximizes the CI itself. Results obtained on all detector channels of a 4 cm LabPET™ located at Sherbrooke show mean error rates of >;10%, 3.66%, 2.96% and 1.45% for the four models, respectively, and are to be compared to the current unique model implementation achieving an error rate of 3.53% for an energy window of 350-650 keV.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.001 | 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".