Sci—Fri AM: Imaging — 08: Comparison of single and dual layer detector blocks for pre‐clinical MRI‐PET
Bibliographic record
Abstract
Our group is developing a small animal PET scanner which would fit in a 7 Tesla MRI scanner to provide simultaneous PET and MR acquisitions and images. There is very little room for the PET detectors and we must use nonmagnetic materials. This presentation describes preliminary work with two PET detectors consisting of LYSO blocks each with two crystal layers: 49 1.67*1.67*6.0 mm on the bottom layer and 36 4.0 mm deep crystals on the top layer. These are mounted on SensL 4*4 arrays of silicon photo-multipliers whose outputs are multiplexed to provide three signals: Energy, X, Y. These detectors were mounted on translation stages and scanned past a 0.25 mm diameter 370 MBq Na-18 source embedded in tissue equivalent plastic. The results were compared with similar single layer crystal blocks with 10 mm deep crystals to assess the advantage of dual layer crystals to reduce radial blurring in a PET scanner with a diameter of only 64 mm. The ability to identify correctly each crystal is reported as its resolvability index, (RI), defined as the (crystal response FWHM)/(crystal's separation) in the crystal identification matrix. For the dual layer block RI =0.44, and for dual layer block RI=0.22. The coincidence response resolution for the single layer block varied from 1.23±0.05 mm at the centre of the scanner to 3.09±0.10 mm at 15.8 mm radius, while the dual layer block varied from 1.31±0.06 to 1.96±0.51 mm over the same range, confirming the Monte-Carlo simulations showing reduced radial blurring.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".