  X-ray photon-counting detectors (PCDs) are drawing an increasing attention in
recent years due to their low noise and energy discrimination capabilities. The
energy/spectral dimension associated with PCDs potentially brings great
benefits such as for material decomposition, beam hardening and metal artifact
reduction, as well as low-dose CT imaging. However, X-ray PCDs are currently
limited by several technical issues, particularly charge splitting (including
charge sharing and K-shell fluorescence re-absorption or escaping) and pulse
pile-up effects which distort the energy spectrum and compromise the data
quality. Correction of raw PCD measurements with hardware improvement and
analytic modeling is rather expensive and complicated. Hence, here we proposed
a deep neural network based PCD data correction approach which directly maps
imperfect data to the ideal data in the supervised learning mode. In this work,
we first establish a complete simulation model incorporating the charge
splitting and pulse pile-up effects. The simulated PCD data and the ground
truth counterparts are then fed to a specially designed deep adversarial
network for PCD data correction. Next, the trained network is used to correct
separately generated PCD data. The test results demonstrate that the trained
network successfully recovers the ideal spectrum from the distorted measurement
within $\pm6\%$ relative error. Significant data and image fidelity
improvements are clearly observed in both projection and reconstruction
domains.
