A novel APD-based detector module for multi-modality PET/SPECT/CT scanners
Bibliographic record
Abstract
The lack of anatomical information in SPECT and PET images is one of the major factors limiting the ability to localize and accurately quantify radionuclide uptake in small regions of interest, This problem could be resolved by using multi-modality scanners having the capability to acquire anatomical and functional images simultaneously. The feasibility of a novel detector suitable for detecting high-energy annihilation radiation in PET, medium-energy /spl gamma/-rays in SPECT and low-energy X-rays in transmission CT is demonstrated and its performance is evaluated for potential use in multi-modality PET/SPECT/CT imaging. The proposed detector consists of a thin CsI(Tl) scintillator sitting on top of a deep GSO/LSO pair read out by an avalanche photodiode. The GSO/LSO pair provides depth-of-interaction information for 511 keV detection in PET, while the thin CsI(Tl) that is essentially transparent to annihilation radiation is used for detecting lower energy X- and /spl gamma/-rays. The detector performance is compared to that of an LSO/YSO phoswich. Although the implementation of the proposed GSO/LSO/CsI(Tl) detector raises special problems that increase complexity, it generally outperforms the LSO/YSO phoswich for simultaneous PET, SPECT and CT imaging.
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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 | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".