First Results From the High-Resolution mouseSPECT Annular Scintillation Camera
Bibliographic record
Abstract
High-resolution single-photon emission computed tomography (SPECT) imaging in small animals tends to use long imaging times and large injected doses due to the poor sensitivity of single pinhole gamma cameras. To increase sensitivity while maintaining spatial resolution, we designed and constructed a multipinhole collimator array to replace the parallel hole collimators of a Ceraspect human SPECT brain scanner. The Ceraspect scanner is composed of an annular NaI(TI) crystal within which the eight pinhole collimators (1-mm-diameter holes) rotate while projecting nonoverlapping images of the object onto the stationary annular crystal. In this manner, only one-eighth of a collimator rotation is required to acquire a full circle orbit tomographic data set. The imaging field of view (FOV) has a diameter of 25.6 mm in the transverse direction, which is sufficient to encompass a mouse in the transverse direction. The axial FOV is 25.6 mm at the center of the FOV and 13.9 mm at the edge of the transverse FOV. Data are currently acquired in step-and-shoot mode; however, the system is capable of list mode acquisition with the collimator continuously rotating. Images are reconstructed using a cone-beam ordered subsets expectation maximization method. The reconstructed spatial resolution of the system is 1.7 mm and the sensitivity at the center of the FOV is 13.8 cps/microCi. A whole-body bone scan of a mouse injected with [Tc-99 m]MDP clearly revealed skeletal structures such as the ribs and vertebral bodies. These preliminary results suggest that this approach is a good tradeoff between resolution and sensitivity and, with further refinement, may permit dynamic imaging in living animals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".