Investigation of analog charge multiplexing schemes for SiPM based PET block detectors
Bibliographic record
Abstract
Reducing the number of output channels in pixelated positron emission tomography (PET) detectors is an effective way to minimize cost and complexity while minimizing the impact on detector performance. This paper compares the system performance of two multiplexing schemes by using both simulation and experimental studies, with respect to spatial, time and energy resolutions. Simulations were performed using the SPICE environment to investigate differences in resulting flood histograms and rising edge slopes. Experiments were performed using lutetium-yttrium oxyorthosilicate (LYSO) crystals coupled to a SensL ArraySL-4 silicon photomultiplier (SiPM) connected to interchangeable circuit boards containing the two multiplexing schemes of interest. Three crystal configurations were tested: a single crystal element (3×3×20 mm(3)), 2×2 array (crystal pitch: 2×2) and 6×6 array (crystal pitch: 2.1×2.1×20 mm(3)). Good agreement was found between the simulations and experiment results. It is found that the capacitive multiplexing is able to achieve an improved time resolution of good uniformity (average of 1.11 ± 0.01 ns and 1.90 ± 0.03 ns for the arrays, respectively) and crystal separation, compared to the resistive multiplexing (average of 1.95 ± 0.03 ns and 3.33 ± 0.10 ns). On the other hand, the resistive multiplexing demonstrates slightly improved energy resolution (11 ± 0.1% and 22 ± 0.6%, compared to 12 ± 0.1% and 24 ± 0.4% for the capacitive array), which is believed to be caused by the RC circuit formed between the splitting capacitors and the input impedance of amplifiers. The relevancy of this work to the PET block detector design using SiPM arrays is also discussed, including light sharing, edge compression and gain variation among SiPM pixels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".