Studying the effects of metallic components of PET-insert on PET and MRI performance due to gradient switching
Bibliographic record
Abstract
Abstract Inserting positron emission tomography (PET) detection modules inside an MRI bore imposes extra challenges owing to the behavior of metallic materials in a strong magnetic field. The metallic parts even when placed outside an MRI field of view may not only disturb MRI performance, but could also increase temperature and vibrations, leading to premature failure of PET electronics. To investigate the compatibility of detection modules inside 3 T, 7 T and 9.4 T MRI bore, a theoretical study of the metal induced artifacts originating from component materials of electronic circuit is presented. The LabPET II detection module and a modified version of it in which the connector was replaced by ball grid array (BGA) were studied. In addition, the effect of eddy current and the associated heat loss on the PET detection module have been examined using COMSOL Multiphysics ® simulations for 10 kHz and 100 kHz gradient switching. Results show that displacement artifacts resulting from the presence of small amounts of ferromagnetic metal and the heating effects of metal due to gradient switching can be compensated by using the slightly modified LabPET II detection module. Thus, the LabPET II system would be MR-compatible with some minor adjustments to operate effectively inside an MRI bore without interfering with its performance.
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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) | 0.000 | 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) | 0.001 | 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".