  Photon counting spectral CT (PCCT) can produce reconstructed attenuation maps
in different energy channels, reflecting energy properties of the scanned
object. Due to the limited photon numbers and the non-ideal detector response
of each energy channel, the reconstructed images usually contain much noise.
With the development of Deep Learning (DL) technique, different kinds of
DL-based models have been proposed for noise reduction. However, most of the
models require clean data set as the training labels, which are not always
available in medical imaging field. Inspiring by the similarities of each
channel's reconstructed image, we proposed a self-supervised learning based
PCCT image enhancement framework via multi-spectral channels (S2MS). In S2MS
framework, both the input and output labels are noisy images. Specifically, one
single channel image was used as output while images of other single channels
and channel-sum image were used as input to train the network, which can fully
use the spectral data information without extra cost. The simulation results
based on the AAPM Low-dose CT Challenge database showed that the proposed S2MS
model can suppress the noise and preserve details more effectively in
comparison with the traditional DL models, which has potential to improve the
image quality of PCCT in clinical applications.
