Simulation Study of Channel Reduction in Side-readout Slab-based Monolithic Crystals used in PET Detector Modules
Bibliographic record
Abstract
In positron emission tomography (PET), monolithic crystals have been used as an alternative to the conventional pixelated design to maintain high spatial resolution and improve energy/timing resolutions. Recent studies have proposed an approach using layers of thin crystal slabs with 4-side photosensor readout to address the challenges in thick monolithic crystals. This approach reduces the complex 3D calibration to 2D and maintains spatial resolution when total crystal thickness increases by having separate readouts for each layer.This approach is promising, with prototype detectors demonstrating high spatial, energy, and timing resolutions. However, the need to fully cover four sides of the crystal slabs with photosensors or not remains to be investigated. To address this concern, in this study, we explore alternative designs that use larger-pitch sensor arrays or more distant individual sensors while still maintaining sufficient resolutions. We used the GATE simulation toolkit to study the effect of different configurations, e.g., crystal sizes, sensor sizes, and sensor density, on the resolutions of a single crystal slab. Our simulation results suggest that spatial resolution is scalable with crystal width, enabling the estimation of spatial resolutions for different crystal sizes. Additionally, reducing the number of sensors using a larger-pitch sensor array still maintained spatial resolution, making it possible to propose more efficient sensor sizes and quantities to facilitate the implementation of this approach in actual PET scanners.
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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 | 0.001 | 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".