Sci‐Sat AM(1): Imaging‐08: Small animal APD PET detector with submillimetric resolution for molecular imaging
Bibliographic record
Abstract
Visualization and quantification of biological processes in mice, the preferred animal model in most preclinical studies, require the best possible spatial resolution in positron emission tomography (PET). A new 64-channel avalanche photodiode (APD) detector module was developed to achieve submillimeter spatial resolution for this purpose. The module consists of dual 4 × 8 APD arrays mounted in a custom ceramic holder. Individual APD pixels having an active area of 1.1 × 1.1 mm2 at a 1.2 mm pitch can be fitted to an 8 × 8 LYSO scintillator block designed to accommodate one-to-one coupling. An analog test board with four 16-channel preamplifier ASICs was designed to be interfaced with the existing LabPET digital processing electronics. At a standard APD operating bias, a mean energy resolution of 27.5 ± 0.6% was typically obtained at 511 keV with a relative standard deviation of 13.8% in signal amplitude for the 64 individual pixels. Crosstalk between pixels was found to be well below the typical lower energy threshold used for PET imaging applications. With two modules in coincidence, a global timing resolution of 5.0 ns FWHM was measured. Finally, an intrinsic spatial resolution of 0.8 mm FWHM was measured by sweeping a 22Na point source between two detector arrays. The proposed detector module demonstrates promising characteristics for dedicated mouse PET imaging at submillimiter resolution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.038 | 0.013 |
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".