Geometry Optimization of a Dual-Layer Offset Detector for Use in Simultaneous PET/MR Neuroimaging
Bibliographic record
Abstract
PET scanners suffer from a loss of resolution due to parallax error. Depth-of-interaction detectors, such as duallayer offset (DLO) detectors, mitigate the loss of resolution. We are designing a DLO brain dedicated PET insert that fits into the Siemens Magnetom 7T brain MR scanner. A wide range of DLO detector geometries was evaluated through Monte Carlo simulations in this paper. The total detector thickness was varied from 10 to 30 mm and for each total thickness, various layer thickness ratios and crystal widths in the range of 0.5-4 mm were examined. The effects of each detector geometry on radial mispositioning of the incident photons, detection efficiency ratio between the two layers, coincidence response functions (CRFs), system spatial resolution, and sensitivity were studied. Optimum layer thickness ratio for each total thickness is discussed. Analysis of CRFs showed that the improvement in the CRF of a DLO PET scanner with a total detector thickness of larger than 10 mm is lower for detectors with a crystal width of smaller than 2 mm than for detectors with a larger crystal width. Estimated spatial resolution and sensitivity of a PET scanner with each suggested detector geometry are also reported.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".