RF shielding materials for highly-integrated PET/MRI systems
Bibliographic record
Abstract
Abstract Integration of magnetic resonance imaging (MRI) and positron emission tomography (PET) into a simultaneous device calls for adaptations of the radio frequency (RF) shielding concept. Conventional PET module housings fully encase the entire PET detector to reduce mutual interference. Excluding passive components, i.e. scintillators, from the housings, offers integration advantages, e.g. by reducing the overall housing volume or utilizing bigger scintillators. However, locating the scintillator outside of the RF shielding requires an optically transparent RF shielding interface between the scintillators and the photodetector to close the aperture. Therefore, a careful evaluation and selection of RF materials is essential to ensure an excellent PET/MRI system performance. To this end, we examined 10 materials (coated glasses, coated foils, meshes). The shielding effectiveness (SE) was evaluated at 100 and 300 MHz. PET performance was tested for single event registration and coincident events by integrating the material into the PET detector stack between the digital silicon photomultiplier photodetector array and one-to-one coupled scintillator. We determined photon attenuation (PA), energy resolution (dE/E), and coincidence resolving time (CRT) and compared to reference measurements for each material group. MRI compatibility was assessed by analyzing the material influence on the main magnetic field (B 0 ) homogeneity. The coated glasses and foils exhibited SEs of up to 25 dB at 300 MHz. Both had a PA < 23% with dE/E and CRT comparable to the reference measurements, and no measurable impact on the B 0 field was registered. The meshes exhibited higher SEs up to 56 dB, but also a PA > 58% with a higher impact on dE/E and CRT. Only one mesh affected B 0 homogeneity. Overall, we recommend the coated foil HS 9400 for integration concepts as it exhibited a good performance with SE = 25 dB, PA = 22%, resulting in a PET performance of dE/E = 12% and CRT = 274 ps.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".