Direct energy binning for photon counting detectors: Simulation study

Abstract

BackgroundPhoton counting detectors (PCDs) for x‐ray computed tomography (CT) face spectral distortion from pulse pileup and charge sharing. The photon counting scheme used by many PCDs is threshold–subtract (TS) with pulse height analysis (PHA), where each counter counts up‐crossing events when pulses exceed an energy threshold. PCD data are not Poisson‐distributed due to charge sharing and pulse pileup, but the counting statistics have never been studied yet.PurposeThe objectives of this study were (1) to propose a modified photon counting scheme, direct energy binning (DB), that is expected to be robust against pulse pileup; (2) to assess the performance of DB compared to TS; and (3) to evaluate its counting statistics.MethodsWith DB scheme, counter k starts a timer upon an up‐crossing event of energy threshold k, and adds a count only if the next higher energy threshold (k+1) was not crossed within a short time window (hence, the pulse peak belongs to the energy bin k). We used Monte Carlo (MC) simulation and assessed count‐rate curves and count‐rate‐dependent spectral imaging task performance for conventional CT imaging as well as water thickness estimation, water–bone material decomposition, and K‐edge imaging with tungsten as the K‐edge material. We also assessed count‐rate‐dependent measurement statistics such as expectation, variance, and covariance of total counts as well as energy bin outputs. The agreement with counting statistics models was also evaluated.ResultsThe DB scheme improved the count‐rate curve, that is, mean measured counts as a function of input count‐rate, and peaked with 59% higher count‐rate capability than the TS scheme (3.5 × 108 counts per second (cps)/mm2 versus 2.3 × 108 cps/mm2). The Cramér–Rao lower bounds (CRLB) of the variance of basis line integrals estimation for DB was better than those for TS by 2% for the conventional CT imaging, 30% for water–bone material decomposition, and 32% for K‐edge imaging at 1000 mA (at 7.3 × 107 cps/sub‐pixel after charge sharing). When count‐rates were lower, PCD data statistics were dominated by charge sharing: the variance of total counts and lower energy bins was larger than the mean counts; the covariance of bin data was positive and non‐zero. When count‐rates were higher, PCD data statistics were dominated by pulse pileup: the variance of data was lower than the mean; the covariance of bin data was negative. The transition between the two regimes occurred smoothly, and pulse pileup dominated the statistics ≥400 mA (when the count‐rate after charge sharing was 2.9 × 107 cps/sub‐pixel and the probability of count‐loss for DB was 37%). Both DB and TS had good agreement with Yu–Fessler's models of total counts; however, DB had a better agreement with Wang's variance and covariance models for energy bin data than TS did.ConclusionsThe proposed DB scheme had several advantages over TS. At low to moderate flux, DB could improve the resilience of PCDs to pulse pileup. Counting statistics deviated from the Poisson distribution due to charge sharing for lower count‐rate conditions and pulse pileup for higher count‐rate conditions.