Improved Edge Pixel Resolution in Modular PET Detectors with Partly Segmented Light Guides
Bibliographic record
Abstract
BACKGROUND: The asymmetric distribution of optical photons near the edges of Positron Emission Tomography (PET) sensor modules introduces errors in the coordinate reconstruction of scintillation events when center-of-gravity (CoG) algorithms are utilized. This issue, sometimes referred to as the "edge effect", results in overlap of crystal pixel signatures in flood maps and potential image artifacts in reconstructed PET images. METHODS: Partly segmented 5 mm thick borosilicate light guides with slits cut parallel to the edges are filled with barium sulfate to restrict the spread of optical photons near the edges of the light guide. Data acquisitions are performed using single PET sensor modules in coincidence, both with single and multiplexed channel readout. CoG and truncated center-of-gravity (TCoG) methods are used for coordinate reconstruction. RESULTS: A 22 × 22 array of crystal signatures are distinguishable on crystal flood maps produced using sensor modules with solid light guides and 24 × 24 arrays can be identified when using a partly segmented light guide. The pixel resolution around the edges and corners of the flood map is further improved when TCoG is used for coordinate reconstruction. SUMMARY: We show that the introduction of a partly segmented light guide greatly improves coordinate reconstruction accuracy at the edges of a sensor module. In addition, it is demonstrated that the partly segmented light guides can be used in parallel with other proposed methods designed to fix the "edge effect", including TCoG, to further coordinate reconstruction improve accuracy and crystal flood map quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".