Gaussian position-weighted center of gravity algorithm for multiplexed readout
Bibliographic record
Abstract
Readout signal multiplexing is a commonly used method to reduce the electronics cost in positron emission tomography (PET) systems, and the calculation of the scintillation coordinates typically is done by using a center of gravity (CoG) technique due to its simplicity and ease of implementation. This comes with a drawback, since CoG has a non-linear response at the periphery of the detector due to the lack of weights beyond the detector. Detectors with multiplexed readout that are based on finely segmented scintillators and coarsely segmented photosensors are known to suffer from the so-called edge effect where a pile-up of the reconstructed coordinates from the edge crystals is observed. This may lead to incorrect assignment of the events to crystal pixels and result in the formation of erroneous lines of response causing a degradation of spatial resolution and reduction of image contrast. To overcome the abovementioned limitations in gamma-ray detectors with multiplexed photosensor readout, we propose to use a modified Gaussian position-weighted center of gravity (PW-CoG) technique for the calculation of gamma-ray interaction position. Here, the proposed method is applied to PET detectors with 24 [Formula: see text] 24 LYSO crystals read out by 8 [Formula: see text] 8 SiPM array with 64:16 row/column multiplexing. Furthermore, we compared the modified Gaussian PW-CoG and truncated center of gravity coordinate reconstruction methods. It was observed that both algorithms resolve peaks corresponding to events registered in the crystal pixels on the periphery of the crystal array. However peak-to-valley ratios and crystal resolvability metrics for the PW-CoG algorithm are generally greater.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".