Deep learning extracts photon-by-photon arrival times from scintillation detector waveforms using unsupervised training with a physically informed model, enabling improved timing resolution and photon classification in experiments.
Scintillator-integrated microchannel plate photomultiplier tubes for ultrafast timing over keV –GeV energy scales ,
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Proposes TOF-decomp ADMM that splits fast- and slow-CTR log-likelihood terms under a constraint to balance contributions and enable improved contrast-noise trade-offs via early stopping in multi-kernel TOF-PET reconstruction.
citing papers explorer
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Machine learning enables experimental access to photon-by-photon arrival times in scintillation detectors
Deep learning extracts photon-by-photon arrival times from scintillation detector waveforms using unsupervised training with a physically informed model, enabling improved timing resolution and photon classification in experiments.
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Multi-Kernel TOF-PET Image Reconstruction Using ADMM
Proposes TOF-decomp ADMM that splits fast- and slow-CTR log-likelihood terms under a constraint to balance contributions and enable improved contrast-noise trade-offs via early stopping in multi-kernel TOF-PET reconstruction.