Performance investigation of LabPET II detector technology in an MRI-like environment
Bibliographic record
Abstract
The EMI-compatibility of the LabPET II detection module (DM) to develop a high-resolution simultaneous PET/MRI system is investigated. The experimental set-up evaluates the performance of two LabPET II DMs in close proximity to RF coils excited at three different frequencies mimicking the electromagnetic environments of 3 T, 7 T, and 9.4 T MRI scanners. A gradient coil, with switching frequency from 10 kHz to 100 kHz, also surrounds one of the DMs to investigate the effects of the gradient field on the individual detector performance, such as the baseline of the DC-voltage and noise level along with both the energy and coincidence time resolutions. Measurements demonstrate a position shift of the energy photopeaks (⩽9%) and a slight deterioration of the energy and coincidence time resolutions in the presence of electromagnetic interferences from the gradient and RF coils. The electromagnetic interferences cause an average degradation of up to ~50% of the energy resolution (in time-over-threshold spectra) and up to 18% of the timing resolution. Based on these results, a modified version of the DM, including a composite shielding as well as an improved heat pipe-based cooling mechanism, capable of stabilizing the temperature of the DM at ~40 °C, is proposed and investigated. This shielded version shows no evidence of performance degradation inside an MRI-like environment. The experimental results demonstrate that a properly shielded version of the LabPET II DM is a viable candidate for an MR-compatible PET scanner.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".