Evaluation of easily implementable inter-crystal scatter recovery schemes in high-resolution PET imaging
Bibliographic record
Abstract
The detection efficiency of high-resolution PET systems based on arrays of pixelated detectors with individual crystal readout can be increased by lowering the energy threshold to recover low-energy first Compton events. However, allowing such low-energy events also results in inter-crystal scatter processes that generate triple coincidences (or "triplets") with ambiguous line-of-response (LORs). Whereas the problem has been investigated extensively by Monte Carlo simulations, little experimental data exist to confirm findings. Taking advantage of the fully parallel data processing and acquisition system of the LabPET scanner, every hits belonging to multiple events, which are normally discarded at an early stage in the data processing, were recorded in the research list mode with the relevant information (crystal position, energy and timestamp). Four different algorithms that can be readily implemented in the real-time coincidence processor were then used to select the LORs and build the corresponding acquisition sinograms. Images of a NEMA phantom were then reconstructed to assess the consequences of the four different recovery algorithms on detection efficiency and image quality. One of the main goals of the study was to determine whether simple triplets recovery schemes could be used as a solution to increase system detection efficiency while preserving image accuracy with real data sets. Results indicate that the addition of triple coincidences in the PET image formation process has an interesting potential to increase detector efficiency without significantly degrading image quality.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".