Count rate performance of brain-dedicated PET scanners: a Monte Carlo simulation study
Bibliographic record
Abstract
Abstract In recent years, there has been a renewed interest in brain-dedicated PET imaging systems, particularly in the context of combined PET/MR imaging. We are currently designing a brain-dedicated PET insert suitable for an ultra-high field brain-dedicated MR scanner, the Siemens Magnetom 7T MR scanner. In this paper, an investigation on the count rate performance of several possible detectors through a series of Monte Carlo simulations is reported. Brain-dedicated PET scanners with a lutetium oxyorthosilicate scintillator and a detector area of 0.04 (1 crystal per detector) to 101.37 (2500 crystals per detector) cm 2 , detector thickness of 10 to 20 mm and a fixed crystal pitch of ~2 mm were simulated. The count rate performance of each scanner was evaluated as a function of detector deadtime type and constant, coincidence timing window and lower level discriminator. Also, the effects of activity outside the field-of-view (FOV) on the count rate performance of each scanner were studied. For each detector geometry and performance metric, the scanner singles rate, scanner sensitivity and noise equivalent count rate as a function of activity in the FOV were measured. It was seen that scanners with detectors comprised a few crystal elements showed reduced scanner sensitivity due to a high number of inter-detector scattering. The count rate performance of scanners with large detectors, on the other hand, was mainly determined by the deadtime properties of the detectors. A model for the count rate performance of the scanner with each studied detector is presented in this work.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".