  Photon counting detectors (PCDs) bring valuable advantages to diagnostic
computed tomography (CT), including lower noise and higher resolution than
energy integrating detectors. However, there are still several nonideal factors
preventing PCDs from meeting people's expectations, for example, charge sharing
and pile up. In this paper, we did some preliminary work on charge sharing and
conducted an experimental study using an XCounter PCD to compare the effects of
no anti-coincidence, anti-coincidence by hardware and charge sharing
compensation by a deep learning method. In our results, a smaller bias and
standard deviation are obtained from deep learning method than directly from
no-anti-coincidence mode of the detector. Our network also outperforms the
anti-coincidence mode of the detector in the low energy bin and has smaller
standard deviation in the high energy bin. The results validate that a deep
learning method is suitable to compensate for charge sharing.
