Improved LabPET Detectors Using <formula formulatype="inline"><tex Notation="TeX">${\rm Lu}_{1.8}{\rm Gd}_{0.2}{\rm SiO}_{5}\!\!:{\rm Ce}$</tex></formula> (LGSO) Scintillator Blocks
Bibliographic record
Abstract
The scintillator is one of the key building blocks that critically determine the physical performance of PET detectors. The quest for scintillation crystals with improved characteristics has been crucial in designing scanners with superior imaging performance. Recently, it was shown that the decay time constant of high lutetium content Lu1.8Gd0.2SiO5: Ce (LGSO) scintillators can be adjusted by varying the cerium concentration from 0.025 mol% to 0.75 mol%, thus providing interesting characteristics for phoswich detectors. The high light output (90%-120% NaI) and the improved spectral match of these scintillators with avalanche photodiode (APD) readout promise superior energy and timing resolutions. Moreover, their improved mechanical properties, as compared to conventional LGSO ( Lu0.4Gd1.6SiO5: Ce), make block array manufacturing readily feasible. To verify these assumptions, new phoswich block arrays made of LGSO-90%Lu with low and high mol% Ce concentrations were fabricated and assembled into modules dedicated to the LabPET scanner. Typical crystal decay time constants were 31 ns and 47 ns, respectively. Phoswich crystal identification performed using a digital pulse shape discrimination algorithm yielded an average 8% error. At 511 keV, an energy resolution of 17-21% was obtained, while coincidence timing resolution between 4.6 ns and 5.2 ns was achieved. The characteristics of this new LGSO-based phoswich detector module are expected to improve the LabPET scanner performance. The higher stopping power would increase the detection efficiency. The better timing resolution would also allow the use of a narrower coincidence window, thus minimizing the random event rate. Altogether, these two improvements will significantly enhance the noise equivalent count rate performance of an all LGSO-based LabPET scanner.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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".