Evaluation of the Spatial Resolution Improvement of the MicroPET R4 Scanner with a Wobbling Bed
Bibliographic record
Abstract
The MicroPET R4 scanner was designed for imaging small rodents like mice and rats. In many cases its spatial resolution is not good enough to perform the required task. We have implemented an eccentric motion (commonly referred to as wobbling) which is applied to the bed during scanning. The bed can wobble with a period of 8-15 seconds and a variable radius between 0.25 and 1.75 mm in 0.25 mm steps. The program which histograms the list-mode data was re-written to increase the spatial sampling by incorporating the wobble position in the sinograms. The corrections for the dwell time, apparent crystal location, and crystal-pair efficiency are applied within the program. A series of scans was performed to decide on the optimum wobble radius; it was found to be 1.50 mm. Another series of scans was performed during which a source was moved 0.25 mm between scans with and without the optimal wobble motion. The peak-to-valley ratio between two Na-22 point sources 4.0 mm apart improved from 1.75 in the conventional mode to 2.26 during wobbled scans applying a ramp filter. The bed wobbling mechanism could be added to the microPET R4 or P4 scanners without any major change and without compromising any imaging modes. The improved spatial resolution may represent a cost-effective upgrade to a trade in and purchase of the newer higher performance scanner.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".