Evaluation of the SensL SPMMatrix for use as a detector for PET and gamma camera applications
Bibliographic record
Abstract
The SPMMatrix from SensL (SensL, Cork, Ireland) is a large area photodetector consisting of a 4 × 4 array of SensL SPMArray4 detectors, each a 4 × 4 array of silicon photomultiplier (SiPM) pixels, giving a total of 256 SiPM pixels. In addition, the device has 32 amplifiers and analog-to-digital conversion (ADC) channels and a FPGA-based data acquisition board The anodes of the SiPMs are chained together according to an array/pixel wiring scheme developed by SensL to reduce the number of readout electronics channels to 32. In this work we conducted a preliminary evaluation of the SPMMatrix device to assess its suitability for PET and gamma camera applications. One commercially manufactured 4×4 array of 3.17 × 3.17 × 10 mm3LYSO crystals was coupled to one of the 4×4 SiPM pixel arrays, effectively giving a one-to-one coupling of the scintillator crystal and SiPM pixels. A custom data acquisition program that allowed acquisition of all 32 ADC channels was used to acquire data from the device. A68Ge source was used for all testing. High quality flood images were obtained from the SPMMatrix device, with all crystals being well resolved Two methods were investigated for determining the energy resolution. The better method, using the hardware sum of the pixels in one SPMArray4 detector, gave an energy resolution of 17%. The resolution degraded to 21% when the energy value was calculated by a software sum of the pixel values. This decrease in energy resolution is likely due to the contribution of dark noise from the pixels in the other arrays and due to the array/pixel multiplexing strategy. In comparison, the same L YSO array tested with a single SPMArray4 detector and NIM electronics gave an energy resolution of 14.6%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".