First images from a combined PET and field-cycled MRI system
Bibliographic record
Abstract
1530 Objectives Recent efforts to combine PET and MRI have either modified PET detectors to make them compatible with magnetic fields or changed the MR system to enable the use of conventional PMT-based PET detectors. The authors present the first hybrid images from a system that combines conventional PMT-based PET with field-cycled MRI (FCMRI). Methods The FCMRI system was operated by sequentially pulsing a 0.3-T polarizing magnet and a 94-mT readout magnet. During the cooling period of the resistive magnets, all magnetic fields were turned off to permit the operation of PMTs for PET imaging. Two PET detectors, each containing a BGO scintillator cut into 8x8 crystals with 6.5x5.5x30-mm3 pitch, were coupled to four PMTs and inserted into a 9-cm opening in the FCMRI system. A phantom consisting of a triangular puck containing three Na-22 point sources embedded into an onion was imaged. Since only two PET detectors were available, the phantom was rotated in a step and shot method and PET data were recombined post-acquisition. Results PET and MR images were superimposed. The layers of the onion are visible in the MR image and the positions of the three point sources are visible in the PET image. No major streak artifacts appear in the PET image and the MR image has no visible phase encode or ghosting artifacts. Conclusions The authors have demonstrated that dual modality PET/FCMRI imaging can be achieved without significant interference between modalities. PET performance was limited by the use of clinical detectors with coarse crystal pitch. Designs for a future PET/FCMRI system, currently in development, are based on the integration of a commercial small-animal PET system and a higher field-strength FCMRI system.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".