Energy and position dependence of scatter functions in high resolution PET
Bibliographic record
Abstract
The authors have shown in previous works that distinct non-stationary analytical scatter kernels can be extracted from line source measurements and used to independently subtract object scatter and subtract or restore detector scatter in high resolution PET. In this work, the dependence of the scatter components on energy threshold and source position was investigated. Line source measurements were acquired in multispectral mode using the Sherbrooke PET simulator. Scatter parameters were extracted from data cumulated in energy windows with a lower threshold varying from 129 keV to 516 keV in steps of 42 keV, and a fixed upper threshold of 644 keV. Decreasing the lower threshold from 344 keV to 129 keV increases the trues by only 25%, but increases object scatter by 136% and almost triples detector scatter. A gain in efficiency by a factor of 2 or more would result from recovering the latter by restoration in the broad window. The intensity and shape of the scatter functions for both object and detector are shown to have a significant dependence on energy and position. This dependence needs to be taken into account in the design of kernels for accurate scatter correction over a broad energy range.>
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".