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

Abstract

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 $1\times 1\times 10$ mm3 LYSO:Ce crystal paired with a 180 ps single photon timing resolution (SPTR) photodetector and a fast $1\times 1\times 3$ mm3 LYSO:Ce:rCa crystal paired with a 35 ps SPTR photodetector. For a single photon avalanche diode with DCR over 1 Hz/ $\mu {\mathrm{ m}}^{2}$ , 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.