Scatter restoration in PET imaging
Bibliographic record
Abstract
Positron emission tomography (PET) is a quantitative tool having the capability of estimating physiological parameters in vivo. However, in order for these parameters to be accurate, PET data need to be corrected for image degrading effects such as scatter. The amount of scatter and its axial and transaxial distributions in the images depend on the position of the emitting sites, on the scattering object and on the collimators. Generally scatter functions are determined from point sources, or by extrapolation or the radioactivity distribution from out of the object in the image, or by analytical estimation of single scatter based on emission, transmission and photon detection. In this work, scatter fraction is determined by Monte Carlo calculations based on PET images in humans and in rats measured with the Philips Allegro scanner and the Sherbrooke small animal scanner, respectively. Assuming the image slice is made of tissue only, the scatter fraction estimated as a function of the number of Compton interactions in human (rat): no scatter: 9.75% (66.16%), single scatter: 21.92% (26.96%), double scatter (both photons scatter once): 12.42% (2.71%), multiple scatter: 55.89% (4.18%). Moreover, the distribution of each of these types of scatter has its characteristics, generally having its maximum corresponding to source location. In conclusion, scatter functions need to be determined as a function of the type of scatter in order to process an accurate scatter correction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".