Sci‐Fri PM Imaging‐10: LabPETtm: A Second‐Generation APD‐Based Digital Scanner for High‐Resolution Small Animal PET Imaging
Bibliographic record
Abstract
LabPET™ is a novel APD‐based PET scanner with quasi‐individual crystal readout and highly parallel digital architecture for high‐performance in vivo molecular imaging of small animals. It consists of 16 rings, 16.2 cm in diameter, each formed by 192 2×2×(9.9–11.3) mm3 scintillators assembled in LYSO/LGSO phoswich pairs read out by an avalanche photodiode (APD). The field of view extends up to 110 mm in diameter by 37.5 mm axially. The dual‐crystal readout scheme avoids resolution degradation due to light‐ or charge‐sharing and enables very high singles count rates with low dead time. Advanced parallel algorithms for signal processing and analysis implemented in high‐performance programmable devices perform crystal identification, energy discrimination and time stamping of individual pixels in real‐time. Virtually error‐free crystal identification is achieved using an auto‐regressive moving average method. Coincidence events are sorted in real‐time and recorded in list‐mode, together with scanner status information and animal physiological data. The intrinsic spatial resolution (both radial and axial) at the FOV center is 1.2 mm FWHM (2.3 mm FWTM). The 1.2‐mm diameter hot rods can be clearly resolved in images of a resolution phantom. Preliminary time resolutions of 2.5 and 3.9 ns FWHM were obtained respectively for LYSO‐LYSO and LGSO‐LGSO coincidences using an auto‐regressive least‐mean square timing algorithm. The system is implemented with ancillary devices (injectors, life‐sign monitors, blood counter…) controlled through a centralized user‐interface, offering a fully integrated solution for performing demanding molecular imaging investigations with increased throughput in a busy research environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.012 | 0.004 |
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".