Quantifying the Impact of Optical Crosstalk for a Scintillator Based TOF-CT Detector
Bibliographic record
Abstract
Computed Tomography (CT) has witnessed remarkable advancements in the last decade, particularly in the reduction of X-ray exposure, while maintaining high-quality images. A novel concept that enhances contrast-to-noise ratio substitutes the anti-scatter grid with a photon filtering method based on time of flight (ToF). The ToF-CT detector design includes an array of small, optically isolated scintillator crystals, each connected to an independent photosensor, to measure the ToF information of individual X-ray photons. However, the interaction of photons produced by an X-ray with a scintillator crystal can result in the emission of visible light beyond the crystal boundaries, leading to signal contamination and reduced performance through inter-crystal crosstalk. As the crystals become narrower for better spatial resolution, the probability of optical crosstalk is expected to increase. To assess the impact of inter-crystal crosstalk on the sensitivity and spatial resolution of ToF-CT detectors, we conducted a Monte Carlo simulation of scintillation events in the detector and evaluated several crosstalk metrics for various configurations of LYSO crystals connected to SiPM arrays. For a spherical collimated source located in the middle of a central scintillation detector measuring 1x1x2.5 mm3 and a lower energy threshold of 20 keV, the probability of optical crosstalk for one X-ray was found to be 0.12%. These findings indicate that ignoring crosstalk signals can reduce tomograph sensitivity using these detectors, along with a 7% decrease in energy resolution.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".