Improvement of the spatial resolution of the MicroPET R4 scanner by wobbling the bed
Bibliographic record
Abstract
The MicroPET R4 scanner was designed for imaging small rodents such as mice and rats. In many cases the spatial resolution of this system is not sufficient for resolving structures of interest. In order to improve the spatial resolution of the MicroPET R4 through improved spatial sampling, the authors have implemented a variable radius eccentric motion, commonly referred to as wobbling, which is applied to the animal bed during scanning. The wobble motion is incorporated into the sinograms using modified histogramming software, capable of reading the bed wobble position from the list-mode data. The histogramming software corrects the data for the dwell time, apparent crystal location, and crystal-pair efficiency and applies a resolution matching filter. The data acquisition, reconstruction, and image display programs provided from the manufacturer required no modifications. For all studies a wobble period of 8 s was used. The optimal wobble radius was found to be 1.50 mm. The wobbled bed acquisition technique was tested by scanning a resolution phantom and a rat. Images from both studies acquired when using the wobble motion showed an improved spatial resolution when compared with comparable images acquired without the wobble motion. The bed wobbling mechanism can be added to any MicroPET system without major changes and without compromising any imaging modes. Implementing the wobble mechanism may represent a cost-effective method to upgrade the spatial resolution of a MicroPET when compared to the purchase of a newer generation system.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".