Real Time Implementation of a Wiener Filter Based Crystal Identification Algorithm for Photon Counting CT Imaging
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Bibliographic record
Abstract
The recently launched LabPETtrade, a small animal avalanche photodiode (APD)-based PET scanner with quasi-individual readout and massively parallel processing, makes it possible to acquire both computed tomography (CT) and positron emission tomography (PET) images using the same detection system. However, since each APD is coupled to an LYSO/LGSO phoswich scintillator pair, an efficient crystal identification algorithm must be developed to meet the stringent requirements of CT data acquisition in single photon counting mode. We propose a new ultra-fast crystal identification algorithm based on a Wiener filter. This filter instantly recovers crystal parameters by minimizing a linear cost function. A simple one-dimension projection based discrimination is used to identify the scintillating crystal. The algorithm achieves a discrimination rate of 88% for low-energy X-ray photons (~60 keV) at a high count rate >1 M events/sec/channel when implemented in a field programmable gate array.
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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 | 0.001 | 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 it