Dark Count Resilient Time Estimators for Time-of-Flight PET

A better timing resolution for positron emission tomography (PET) detectors adds time-of-flight information to increase the image contrast. The timing resolution can be improved by timestamping multiple photons from a scintillation event and combining the information using the mean or the Gauss-Markov estimator. To circumvent the performance degradation incurred by these estimators at higher dark count rates (DCRs), two dark count resilient algorithms were studied independently in the literature: the filtered Gauss-Markov and the artificial neural networks. This paper presents parametric simulations that compare them under different DCR conditions. We studied two different types of scintillator-based digital silicon photomultipliers : a typical 1×1×10 mm
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LYSO:Ce crystal paired with a 180 ps single photon timing resolution (SPTR) photodetector and a fast 1×1×3 mm
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LYSO:Ce:rCa crystal paired with a 35 ps SPTR photodetector. For a single photon avalanche diode with DCR over 1 Hz/μm
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, simulations show a significant coincidence timing resolution (CTR) gain when using the filtered Gauss-Markov estimator, improving the CTR of the typical detector by 10% and the fast detector by 33% over the unfiltered Gauss-Markov estimator. In all evaluated cases, the filtered Gauss-Markov estimator yields the lowest CTR.