First Results From a High-Resolution Small Animal SiPM PET Insert for PET/MR Imaging at 7T
Bibliographic record
Abstract
We present the initial results from a small animal PET insert designed to be operated inside a 7T MRI. The insert fits within the 114 mm inner diameter of the Bruker BGA-12S gradient coil while accommodating the Bruker 35 mm volume RF coil (outer diameter 60 mm), both used in the Bruker 70/20 MRI systems. The PET insert is a ring comprising 16 detectors. Each detector has a dual-layer offset (DLO) lutetium-yttrium oxyorthsilicate (LYSO) scintillator array read out by two SensL SPMArray4B SiPM arrays. The DLO scintillator has bottom (top) layers of:$22\times 10$($21 \times 9$) crystals of size$1.2 \times 1.2 \times 6$(4) mm3for a total of 409 crystals per block, providing an axial extent of 28.17 mm. The detector outputs are multiplexed to four signals using a custom readout board and digitized using the OpenPET data acquisition platform. The detector flood images successfully resolve over 99% of the crystals, with average energy resolution of 12.5 ± 2.0% at 511 keV. Testing of the PET system inside the MRI showed that the PET insert had no effect on MRI image homogeneity and only a small effect on echo planar images (EPI) signal to noise ratio (SNR) (-9%), with neither PET nor MRI images showing obvious artefacts. These acquisitions used the OpenPET operating in “oscilloscope mode” with USB2.0 interface, allowing a maximum total singles event rate of 280 kcps, strongly limiting the count rate capabilities of the system. The PET radial spatial resolution (as measured with a22Na point source and FBP-3DRP reconstruction) is 1.17 mm at the centre, degrading to 1.86 mm at a 15 mm radial offset. Simultaneous phantom and mouse PET/MR imaging produced good quality images that were free of any obvious artefacts.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".