ARMAX-RLS Parameter-Estimation Crystal Identification in Phoswich PET Detectors
Bibliographic record
Abstract
Some Positron Emission Tomography (PET) scanners achieve improved resolution or Depth-Of-Interaction (DOI) measurement using avalanche photodiode-based phoswich detectors, with different challenges (noise limitation) than traditional photomultiplier tubes (space limitation). DOI measurement is necessary in small-animal PET for parallax mitigation, while side-by-side phoswich detectors improve resolution without an equal increase in electronics complexity. Future improvements in scanner performance now require the improvement of the current Parameter Estimation (PE) digital Crystal Identification (CI) algorithms. Indeed PE CI becomes mandatory to the APD signal analysis of crystals with very similar scintillation properties (decay time difference less than ~15 ns), or when the Signal-to-Noise Ratio (SNR) is degraded, for Compton events with an energy below 150 keV, for instance. PE CI currently relies on one-parameter discrimination of crystal species after a Wiener parametric estimation of a pole-zero model. Neither that nor traditional Pulse-Shape Discrimination (PSD) can correctly accommodate the need for CI of low-energy scattered photons in very similar scintillation materials. This paper studies a Recursive Least-Squares (RLS) PE method based on Auto-Regressive Moving Average with exogenous variable (ARMAX) modeling of the acquisition chain conjugated with simultaneous 3-parameter CI adapted from Vector Quantization (VQ). The RLS algorithm presents a significant improvement for the discrimination of similar materials (~ 80% less primary CI error versus Wiener CI for 15 ns decay time difference) and excellent performance in heavy noise (negligible CI error for 0 dB SNR 30-keV BGO photons versus LSO). Issues remaining include the handling of noise through the exogenous variable and the computational burden, still too high for existing hardware.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".