A study of inter-crystal scatter in dual-layer offset scintillator arrays for brain-dedicated PET scanners
Bibliographic record
Abstract
A dual-layer offset (DLO) detector enables depth-of-interaction (DOI) through light sharing between two layers of scintillation arrays with a single-ended readout (SER) scheme. However, the SER scheme in DLO detectors may lead to a layer misassignment when inter-crystal scattering occurs. The aim of this work is to study inter-crystal scattering and evaluate the effects of layer misidentifications in DLO detectors on the performance of scanners suitable for a brain-dedicated PET insert. The influence of layer misidentification on the coincidence response functions (CRFs) of 3 different DLO detectors with total/front/back layer thicknesses of 15/6/9 mm, 20/8/12 mm, and 25/7.5/17.5 mm and a crystal width of about 3 mm was studied through Monte Carlo simulations. To overcome layer misidentification, we studied a practical DLO detector design in which each layer can be read out independently through a discrete-layer readout (DLR) scheme where light sharing between the layers is avoided. The CRFs of the mentioned DLO detectors assuming SER and DLR were analyzed. To evaluate the effects of layer misidentification on image quality, images of a Derenzo-like phantom were also reconstructed for all DLO and their equivalent single layer PET scanners. Our analysis showed that layer misassignments due to inter-crystal scatter in DLO detectors mainly has effect on the full-width at tenth maximum of the CRFs. According to the reconstructed images of the phantom, no significant improvements in the quality of the images were seen when SER was replaced with DLR. The results suggest that layer misidentification in DLO detectors does not play an important role in the quality of the PET images.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".