An analytical scatter correction for singles-mode transmission data in PET
Bibliographic record
Abstract
We present a scatter correction for singles-mode transmission data and its implementation as part of an iterative image reconstruction algorithm (OSTR). We compared our scatter calculation data with previously validated simulation data for three uniform water cylinders (radii of 25, 30 and 45 mm) using68Ge (a positron emitter) and57Co (122 keV photon emitter) transmission sources. Our scatter correction correctly predicts the contribution from single-scattered (one Compton interaction) photons to the sinogram data. Our correction also provides good agreement for the percent scatter fraction (SF) per sinogram for all phantom sizes and both transmission sources. We applied our scatter correction to experimental data from the microPET Focus 120 small animal scanner for three different sized uniform water cylinders and for a non-uniform phantom consisting of water, Teflon and air. The reconstructed linear attenuation coefficients (mu-values) agreed with expected values to within 4% for both the68Ge and57Co transmission sources and all phantoms. Using a 2.2 GHz processor our scatter correction requires between 7 to 20 minutes of CPU time depending on the phantom size and source used. This extra calculation time does not seem unreasonable considering that, without scatter corrections, errors in the reconstructed mu-values were between 28 to 40% depending on the phantom size and transmission source used. Simple rescaling or segmentation of uncorrected mu-map images does not provide an adequate alternative to scatter correction, since these errors depend on the radial position within an image slice and can be on the order of the difference between the mu-values for water and bone.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".