  Spectral photon-counting X-ray CT (sCT) opens up new possibilities for the
quantitative measurement of materials in an object, compared to conventional
energy-integrating CT or dual energy CT. However, achieving reliable and
accurate material decomposition in sCT is extremely challenging, due to
similarity between different basis materials, strong quantum noise and
photon-counting detector limitations. We propose a novel material decomposition
method that works in a region-wise manner. The method consists in optimizing
basis materials based on spatio-energy segmentation of regions-of-interests
(ROIs) in sCT images and performing a fine material decomposition involving
optimized decomposition matrix and sparsity regularization. The effectiveness
of the proposed method was validated on both digital and physical data. The
results showed that the proposed ROI-wise material decomposition method
presents clearly higher reliability and accuracy compared to common
decomposition methods based on total variation (TV) or L1-norm (lasso)
regularization.
