A Monte Carlo study of the acceptance to scattered events in a depth encoding PET camera
Bibliographic record
Abstract
The authors present a Monte Carlo study of the acceptance to scattered events in a Depth Encoding Large Aperture Camera (DELAC), a hypothetical PET scanner with the capacity to encode the depth-of-interaction (DOI) of incident /spl gamma/-rays. The simulation is initially validated against the measured scatter fraction of the ECAT-953B scanner. It is then used to assess the response to scattered events in a PET camera made of position encoding blocks of the EXACT HR PLUS type, modified to have DOI resolution through a variation in the photopeak pulse height. The detection efficiency for 511 keV /spl gamma/-rays, as well as for those that scattered in the object or left only part of their energy in the block, is studied for several combinations of DOI sensitivities and block thicknesses. The scatter fraction predicted by the simulation for DELACs of various ring radii is compared to that of the ECAT-953B as a function of the energy threshold. The results indicate that the poorer discrimination of object scatters with depth sensitive blocks does not lead to a dramatic increase of the scatter fraction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".