A High-Resolution Semi-Monolithic Slab Detector tailored for Pre-Clinical PET
Bibliographic record
Abstract
Current pre-clinical positron emission tomography (PET) scanners are often based on monolithic detectors due to their high spatial resolution and intrinsic depth-of-interaction (DOI) capabilities to reduce parallax positioning errors. However, the broad light distributions of monolithic designs can lead to drawbacks in the sensor readout and in the processing chain, e.g., dead time as well as pile up. Semi-monolithic slab detectors represent an attractive alternative as they provide a higher optical photon density while allowing a DOI determination. The cost can be assumed competitive with monoliths. This work presents a semi-monolithic detector comprising 1 mm thin high-resolution LYSO slabs coupled to a 12 × 12 channel digital SiPM tile. Currently, a detector spatial resolution (SR) (FWHM of the PSF) of 1.2 / 2.3 mm and a mean absolute error (MAE) of 0.9 / 1.1 mm is achieved in planar-monolithic / DOI direction with a correct slab identification in the planar-segmented direction of 92 % considering a [435 - 585] keV energy window. An energy resolution of 15.2 % and 461 ps coincidence timing resolution (CTR) were obtained.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".