Performance evaluation of SensL SiPM arrays for high-resolution PET
Bibliographic record
Abstract
Silicon photomultipliers (SiPMs) have high gain, excellent timing performance, and are well suited to PET/MRI applications due, in part, to their MR-compatibility and small form factor. Within the constraints of a resistor-based multiplexing circuit, it is useful to evaluate the four generations of SiPM arrays manufactured by SensL: the SPMArray4, ArraySL-4, ArraySM-4, and ArraySB-4. Breakdown voltage and dark current were measured as a function of temperature in two each of the four generations of SensL SiPM arrays. Flood histograms were created with a68Ge-irradiated 9×9 LYSO crystal array at temperatures of 5 °C to 45 °C in 5 °C increments and overvoltages of 2 to 4 V in 0.5 V increments. Measurements of dark current vs. bias voltage increased as temperature increased, with a corresponding increase in the breakdown voltage, Vb. The temperature dependence of Vbis similar between all four generations of SiPM arrays with slopes ranging from 17.0 to 23.8 mV/°C. Notably, the ArraySB-4 has lower values for the breakdown voltage, with Vb= 24 V at 0 °C. Mean energy resolution for individual LYSO crystals showed improvements in each successive generation. The average energy resolution of the ArraySB-4 was 11.9% after correcting for non-linearity in the SiPM pixels. The linearity of the SensL SiPM arrays as a function of temperature and breakdown voltage makes them a suitable choice for a high-resolution, small animal PET/MRI system. Based on its improved resolvability and energy resolution, lower sensitivity to temperature and higher PDE, the ArraySB-4 will be used in our PET system.
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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) | 0.000 | 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) | 0.002 | 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".