Sci-PM Thurs - 02: Spatial resolution in PET and the effect of gamma-ray interaction depth in block detectors
Bibliographic record
Abstract
The spatial resolution in PET is poorer than that of CT or MRI. All modern PET scanners use block detectors, i.e. clusters of scintillation crystals coupled to four photomultiplier tubes(PMTs). Some of the loss of spatial resolution in PET is attributed to the use of block detectors, because a photon that interacts with one crystal in the cluster may be incorrectly positioned, resulting in blurring of the reconstructed image, called the “block effect”. We examined the effect of changing gamma‐ray interaction depth in the scintillation crystals in detectors from the CTI HR+ and GE Advance PET scanners. We postulated that the depth at which the gamma‐ray interacts may contribute to the “block effect” blurring. The “block effect” was measured for both detectors, and it was found to be 1.2 mm for the central crystals and negligible for the edge crystals in the CTI HR+ block. However, it was 0.9 mm in all crystals of the GE Advance detector. In the CTI HR+ detector, a depth dependence on the positioning of the event was observed, as was a dependence on the crystal location (edge vs. centre). In the GE Advance detector, no such dependence was observed. These results suggest that the depth of interaction of an annihilation photon may contribute to the block effect in detectors that use crystals cut from a single scintillation crystal (pseudo‐discrete crystals). In detectors that use discrete crystals no additional blurring as a function of gamma‐ray interaction depth in the detectors was observed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".