Sci‐Fri AM: YIS‐02: Evaluation of the LabPET4 imaging capabilities for in vivo small animal imaging
Bibliographic record
Abstract
Positron Emission Tomography (PET) is a non-invasive technique to visualize metabolic and physiological processes in vivo. Excellent imaging capabilities such as spatial resolution and count rate performance are essential to achieve accurate information about the observed processes. It is for this purpose that the LabPET scanner, an avalanche photodiode (APD)-based fully digital scanner PET scanner, was initially developed. Two variants of the scanner exist: LabPET4 and LabPET8 with 3.75 and 7.5 cm axial lengths respectively. The range of the transaxial FOV is up to 10 cm therefore it can easily accommodate mice and rats. The aim of this work is to evaluate LabPET4 imaging in several phantoms and small animals. Spatial resolution was determined using a point source and hot spots phantoms. The latter were used to assess recovery coefficients (RC) obtained by taking the ratio of hot spot maximum values compared to the biggest spot maximum value. FBP reconstructed tangential/radial resolution is 1.3/1.4 mm FWHM (2.5/2.4 FWTM) at the field of view center. With an Ultra Micro Hot Spot Phantom, 1 mm spots are clearly resolved. Count rate performance was obtained for mouse-size and rat-size phantoms. For mouse phantom, scatter fraction is 18%, noise equivalent count rate (NEC) peak is 120 kcps at 5.6 mCi and true coincidences peak is 215 kcps at 6.6 mCi. Mice and rats were imaged with Na18F and 18FDG. LabPET4 imaging capabilities achieve state-of-the-art requirements for molecular imaging and therefore can provide excellent quality 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.054 | 0.012 |
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".