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Background: Silicon (Si) is a possible sensor material for photon counting detectors (PCDs). A major drawback of Si is that roughly two‐thirds of x‐ray interactions in the diagnostic energy range are Compton scattering. Because Compton scattering is an energy‐insensitive process, it is commonly assumed that Compton events retain little spectral information. Purpose: To quantify how much information can be recovered from Compton scattering events in models of Si PCDs. Methods: We built a simplified model of Si interactions including two interaction mechanisms: photoelectric effect and Compton scattering. We considered three different binning options that represent strategies for handling Compton events: in Compton censoring, all events under 38 keV (the maximum energy possible from Compton scattering for a 120 keV incident photon) were discarded; in Compton counting, all events between 1 and 38 keV were placed into a single bin; in Compton binning, all events were placed into energy bins of uniform width. These were compared to the ideal detector, which always recorded the correct energy (i.e., 100% photoelectric effect). Every photon was assumed to interact once and only once with Si, and the energy bin width was 5 keV. In the primary analysis, the Si detector was irradiated with a 120 kV spectrum filtered by 30 cm of water, with 99.5% of the arriving spectrum above 38 keV so that there was good separation between photoelectric effect and Compton scattering, and the figures of merit were the Cramér–Rao lower bound (CRLB) of the variance of iodine and water basis material decomposition images, as well as the CRLB of virtual monoenergetic images (i.e., linear combinations of material images) that maximize iodine CNR or water CNR. We also constructed a local linear estimator that attains the CRLB. In secondary analyses, we applied other sources of spectral distortion: (1) a nonzero minimum energy threshold; (2) coarser, 10 keV energy bins; and (3) a model of charge sharing. Results: With our chosen spectrum, 67% of the interactions were Compton scattering. Consistent with this, the material decomposition variance for the Compton censoring model, averaged over both basis materials, was 258% greater than the ideal detector. If Compton events carried no spectral information, the Compton counting model would show similar variance. Instead, its basis material variance was 103% greater than the ideal detector, implying that Compton counts indeed carry significant spectral information. The Compton binning model had a basis material variance 60% greater than the ideal detector. The Compton binning model was not affected by a 5 keV minimum energy threshold, but the variance increased from 60% to 107% when charge sharing was included and to 78% with coarser energy bins. For optimized CNR images, the average variance was 149%, 12%, and 10% higher than the ideal detector for the Compton censoring, counting, and binning models, reinforcing the hypothesis that Compton counts are useful for detection tasks and that precise energy assignments are not necessary. Conclusions: Substantial spectral information remains after Compton scattering events in silicon PCDs. [ABSTRACT FROM AUTHOR]

Determining the sequence of Compton scattering and photoelectric absorption events for a Compton camera system through timing information is difficult due to the finite timing resolution of radiation detectors. The conventional method compares the energies of two sequential events and determines the order of these events. The deep learning method can estimate the sequence of Compton scattering followed by the photoelectric effect better than the conventional method because it determines the sequence based on both energy and positional information of the radiation interaction. The initial information of the deep learning models is the position and energy information, and the input data are then processed in the nodes of the hidden layers. In this study, the performance of deep learning models for Compton sequence estimation and the effect of position information on these methods were investigated. The accuracies of the deep learning method and the conventional comparison method were compared. The weights connecting each node were analyzed to evaluate the effects of position and energy information to determine the Compton sequence. [ABSTRACT FROM AUTHOR]