First results from a high-resolution small animal PET insert for PET/MRI imaging
Bibliographic record
Abstract
We have recently completed construction of a high resolution small animal PET insert designed for operation inside a Bruker 7T MRI. The PET insert is designed to achieve a 1 mm spatial resolution in the centre of its field of view (FOV) and fit 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). The PET insert is a ring geometry with a single ring of 16 detectors. Each detector uses a dual layer offset (DLO) LYSO scintillator array (bottom/top layer: 22x10/21x9 of 1.2x1.2x6/4 mm crystals, 409 crystals per block), with total axial extent of 28.3 mm, readout by two SensL SPMArray4B SiPM arrays. The detector outputs are multiplexed to four signals using a custom readout board and digitized using the OpenPET data acquisition platform. Detector flood image quality is sufficient to resolve >99% of the crystals in the system. The average energy resolution of the 6544 crystals is 11.94%+/-1.77% at 511keV. MR compatibility testing of the complete PET system conducted with a 7T Bruker Avance III MRI showed that the operating PET insert had no effect on MRI image homogeneity and only a small effect on EPI signal to noise ratio (SNR) (-15%). Initial PET data were collected using a Ge68 line source with the PET system on the benchtop. For this first acquisition, the OpenPET system was operating in oscilloscope mode, limiting the total singles event rate to 18kcps. The sinogram and initial reconstructed images showed no obvious artefacts. We have recently implemented an OpenPET firmware upgrade that will support a singles rate of 280kcps; this will allow us to acquire first simultaneous phantom and mouse PET/MR images.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".