Experimental Results of Identification and Vector Quantization Algorithms for DOI Measurement in Digital PET Scanners with Phoswich Detectors
Bibliographic record
Abstract
DOI measurement in phoswich PET scanners still relies mostly on traditional Pulse Shape Discrimination (PSD), transposed from analog electronics. PSD performance is limited in two conditions: measurement noise increases the error rate, as with low-energy Compton photons; and phoswich stacking of the newer, fast crystal materials like LSO, LYSO and LuAP show intrinsic low discrimination success. These impairments somewhat limit the widespread use of such stacking, as well as recuperation and treatment of Compton photons. We propose two new algorithms adapted from other fields of electrical engineering, but unused in radiation detection so far, that mostly circumvent these problems: identification, from command-and-control applications, followed by vector quantization, from speech recognition. These algorithms exhibit operational properties that mitigate the above problems. In our previous work, we explained the steps required to adapt the algorithms to DOI application. This paper presents discrimination results for all photons of energy greater than 100 keV detected in any stacking of BGO, LSO, LYSO, LuAP and/or GSO materials. Errors are un-correlated with crystal statistical noise and/or energy resolution, with electronics white noise and with timestamp uncertainty. For all measurements made (N=40,000), the error rate is null, except for Compton discrimination with the faster crystals, where it does not exceed 0.5%. This far surpasses conventional PSD results.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".