Physics-Informed Deep Neural Network for Inter-Crystal Scattering Recovery in ToF-CT
Bibliographic record
Abstract
Inter-crystal scattering (ICS) fundamentally constrains high-resolution Time-of-Flight Computed Tomography (ToF-CT) by enforcing trade-offs between sensitivity and spatial resolution. Lowering energy thresholds to capture more photons exacerbates spatial ambiguity, while raising thresholds to suppress ICS events reduces detection efficiency, an increasingly critical compromise under stringent dose constraints. As an alternative, this paper present a novel physics-informed deep learning approach that fundamentally resolves this dilemma by exploiting spatiotemporal energy deposition patterns. Our approach employs a two-stage architecture: first, a specialized Convolutional Neural Network (CNN) discriminates between primary and scattered energy deposits; subsequently, a Multi-Layer Perceptron (MLP) precisely reconstructs the originating crystal location for identified scatter events, effectively recovering otherwise lost spatial information. Both network designs are informed by empirical characterization of ICS behavior in finely segmented LYSO detectors. Monte Carlo simulations of LYSO-based ToF-CT detector arrays achieved <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$>94 \%$</tex> accuracy in ICS recovery. By transforming previously rejected scatter events into valuable diagnostic information, our method enables operation at lower energy thresholds (20 keV), yielding a 34 % improvement in absolute detection efficiency while maintaining spatial integrity. Quantitative image reconstruction using a modified ACR phantom revealed a 20 % enhancement in spatial resolution alongside substantial improvements in signal-to-noise ratio (SNR), without compromising contrast fidelity.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".