Imaging performance of LabPET APD-based digital PET scanners for pre-clinical research
Bibliographic record
Abstract
The LabPET is an avalanche photodiode (APD) based digital PET scanner with quasi-individual detector read-out and highly parallel electronic architecture for high-performance in vivo molecular imaging of small animals. The scanner is based on LYSO and LGSO scintillation crystals (2×2×12/14 mm3), assembled side-by-side in phoswich pairs read out by an APD. High spatial resolution is achieved through the individual and independent read-out of an individual APD detector for recording impinging annihilation photons. The LabPET exists in three versions, LabPET4 (3.75 cm axial length), LabPET8 (7.5 cm axial length) and LabPET12 (11.4 cm axial length). This paper focuses on the systematic characterization of the three LabPET versions using two different energy window settings to implement a high-efficiency mode (250–650 keV) and a high-resolution mode (350–650 keV) in the most suitable operating conditions. Prior to measurements, a global timing alignment of the scanners and optimization of the APD operating bias have been carried out. Characteristics such as spatial resolution, absolute sensitivity, count rate performance and image quality have been thoroughly investigated following the NEMA NU 4-2008 protocol. Phantom and small animal images were acquired to assess the scanners' suitability for the most demanding imaging tasks in preclinical biomedical research. The three systems achieve the same radial FBP spatial resolution at 5 mm from the field-of-view center: 1.65/3.40 mm (FWHM/FWTM) for an energy threshold of 250 keV and 1.51/2.97 mm for an energy threshold of 350 keV. The absolute sensitivity for an energy window of 250–650 keV is 1.4%/2.6%/4.3% for LabPET4/8/12, respectively. The best count rate performance peaking at 362 kcps is achieved by the LabPET12 with an energy window of 250–650 keV and a mouse phantom (2.5 cm diameter) at an activity of 2.4 MBq ml−1. With the same phantom, the scatter fraction for all scanners is about 17% for an energy threshold of 250 keV and 10% for an energy threshold of 350 keV. The results obtained with two energy window settings confirm the relevance of high-efficiency and high-resolution operating modes to take full advantage of the imaging capabilities of the LabPET scanners for molecular imaging applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".