MLEM Reconstructed Image Resolution from the LabPET Animal Scanner
Bibliographic record
Abstract
Background: PET image resolution is a function of scanner intrinsic resolution and reconstruction method. The purpose of this study was to measure reconstructed image resolution vs. MLEM iterations on the new LabPET 3.6 animal scanner. Methods: A Micro Deluxetrade hot rods phantom filled with an18F solution was scanned for 60 min, and images were reconstructed using 10 to 1000 MLEM iterations. To estimate the image resolution, peak activity values were measured for each rod and compared to the theoretical values of partial-volume recovery obtained by convolving a 2D-Gaussian model with circles of the known rod diameters. Results were confirmed visually by convolving the estimated Gaussian model with a high resolution CT image. Results: FWHM image resolution improved from 2.1 to 1.3 mm with 10 to 1000 MLEM iterations. CT image convolution with this Gaussian model faithfully reproduced the measured resolution in images reconstructed with 200 MLEM iterations. Conclusion: Initial measurement of the LabPET transverse image resolution is consistent with that expected from a system with individual detector readout.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".