Modular PET Sensors with Partly Segmented Light Guides and Single-Channel TCoG Readout
Bibliographic record
Abstract
Many modular PET sensors detect gamma photons using scintillation crystals that are segmented into small pixels by applying a Center of Gravity (CoG) algorithm to the signals output by the sensor module. Such methods reliably reconstruct the positions of scintillation events when optical photons are symmetrically distributed about the point of gamma photon interaction. However, when scintillation events occur at the periphery of the scintillation crystal array, the sensor module boundary prohibits optical photons from spreading out uniformly. This biases the calculated scintillation coordinates towards the center of the module, causing the scintillation pixel signatures on crystal flood maps to overlap with neighbouring pixels. Solutions have been proposed involving modifications to hardware components or coordinate reconstruction software. These include segmented light guides, a greater number of readout channels and modified CoG algorithms. Trials of such changes have been met with varying degrees of success, however little attention has been given to combinations of these methods on coordinate reconstruction accuracy. This research evaluates the coordinate reconstruction accuracy of sensor modules from a low-dose PET camera using single channel readout, a truncated CoG (TCoG) algorithm and a segmented light guide design. Crystal maps and peak profiles show that a combination of TCoG, single channel readout, and a partially segmented light guide produces the most accurate coordinate reconstruction.
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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 | 0.001 |
| 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".