  With the growing technology of photon-counting detectors (PCD), spectral CT
is a widely concerned topic which has the potential of material
differentiation. However, due to some non-ideal factors such as cross talk and
pulse pile-up of the detectors, direct reconstruction from detected spectrum
without any corrections will get a wrong result. Conventional methods try to
model these factors using calibration and make corrections accordingly, but
depend on the preciseness of the model. To solve this problem, in this paper,
we proposed a novel deep learning-based monochromatic image synthesis method
working in sinogram domain. Different from previous deep learning-based methods
aimed at this problem, we designed a novel network architecture according to
the physical model of cross talk, and it can solve this problem better in an
ingenious way. Our method was tested on a cone-beam CT (CBCT) system equipped
with a PCD. After using FDK algorithm on the corrected projection, we got quite
more accurate results with less noise, which showed the feasibility of
monochromatic image synthesis by our method.
