Performance of a PET Insert for High-Resolution Small-Animal PET/MRI at 7 Tesla
Bibliographic record
Abstract
We characterize a compact MR-compatible PET insert for simultaneous preclinical PET/MRI. Although specifically designed with the strict size constraint to fit inside the 114-mm inner diameter of the BGA-12S gradient coil used in the BioSpec 70/20 and 94/20 series of small-animal MRI systems, the insert can easily be installed in any appropriate MRI scanner or used as a stand-alone PET system. Methods: The insert consists of a ring of 16 detector-blocks each made from depth-of-interaction–capable dual-layer-offset arrays of cerium-doped lutetium-yttrium oxyorthosilicate crystals read out by silicon photomultiplier arrays. Scintillator crystal arrays are made from 22 × 10 and 21 × 9 crystals in the bottom and top layers, respectively, with respective layer thicknesses of 6 and 4 mm, arranged with a 1.27-mm pitch, resulting in a useable field of view 28 mm long and about 55 mm wide. Results: Spatial resolution ranged from 1.17 to 1.86 mm full width at half maximum in the radial direction from a radial offset of 0–15 mm. With a 300- to 800-keV energy window, peak sensitivity was 2.2% and noise-equivalent count rate from a mouse-sized phantom at 3.7 MBq was 11.1 kcps and peaked at 20.8 kcps at 14.5 MBq. Phantom imaging showed that features as small as 0.7 mm could be resolved. 18F-FDG PET/MR images of mouse and rat brains showed no signs of intermodality interference and could excellently resolve substructures within the brain. Conclusion: Because of excellent spatial resolvability and lack of intermodality interference, this PET insert will serve as a useful tool for preclinical PET/MR.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".