Energy dependence of nonstationary subtraction-restoration scatter correction in high resolution PET
Bibliographic record
Abstract
In previous works, the determination of object and detector scatter kernels from line source measurements was described and their application in scatter correction was investigated. It was also shown that low energy data contains a large fraction of useful events (true and detector scatter events). In the present work, data acquired in multispectral mode was summed from a varying lower energy threshold ranging from 129-516 keV up to an upper energy level of 644 keV and the line source projections were fitted for extracting the object and detector scatter kernels as a function of energy threshold. These kernels were then used to process scatter by the non-stationary convolution subtraction-restoration method in phantom images. After scatter correction, the detection efficiency is found to increase by up to 64% at the lower threshold of 129 keV, relative to the conventional photopeak energy window (344-644 keV). Whereas contrast and spatial resolution are degraded as the energy discriminator is lowered, such degradation is fully recovered by the 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".