Imaging performance of a PET/SPECT dual modality animal system
Bibliographic record
Abstract
SPECT imaging capability was developed on a LabPET animal scanner to provide an integrated PET/SPECT bimodality imaging environment, supporting a broader range of imaging applications with an expanded library of radiopharmaceuticals. The SPECT imaging mode was enabled by 1) mechanically integrating collimator assemblies in the PET detector ring, and 2) properly tuning the detectors to acquire singles events in the 120-160 keV range. Two interchangeable collimators were designed for performing different imaging applications: a multiple pinhole collimator for its high resolution suitable for mouse imaging, and a slit-slat collimator for its large FOV adequate for rat imaging. To mitigate the non-uniform detector crystal layout in both the azimuthal and axial directions, a helical scan scheme, which combines collimator rotational movement and subject linear motion, was used for more uniformly sampled volume imaging. Unique calibration techniques taking advantage of the PET imaging capability were developed for determining the geometrical parameters of collimators. We report on the design parameters and initial performance assessment of this cost-effective SPECT imaging solution. Phantom and animal images demonstrating the relevance of the system for various imaging tasks in preclinical research are presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".