A Shielding Free PET Insert for High Sensitivity PET/MRI of the Brain
Bibliographic record
Abstract
This work outlines MRI compatibility tests of a shielding free PET insert for high sensitivity, cost effective PET/MRI imaging of the brain. The MRI compatibility of silicon photomultiplier based PET detectors, originally developed for an organ targeted PET camera, was explored through MRI and PET performance. To analyze the effect of passive and active PET modules on MRI performance, T1-weighted gradient echo images and B1 maps were acquired. The effect of passive and active MRI on PET performance was evaluated using, crystal maps, energy spectra, and images of a 22-Na point source. The average mean squared error between central slice B1 maps with and without passive PET detectors was 0.003 and the structural similarity index was 0.975. For the active PET experiment, these values were 0.054 and 0.891, respectively. There was no significant change in coefficient of variation of B1 intensity due to the active state of PET detectors. The PET crystal map results showed that the spatial distribution of events was not affected by the passive B0 field of the MRI or a simultaneous MRI acquisition. The same result was found for the peak position and full width at half maximum of the energy spectra in PET data. No difference was observed between trials in the reconstructed images of a 22 Na point source. Despite the lack of shielding, the MRI compatibility of Radialis PET detectors was supported by our results, suggesting they will be usable in the proposed PET insert for high sensitivity PET/MRI of the brain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".