ROI-Wise Material Decomposition in Spectral Photon-Counting CT.

Spectral photon-counting X-ray computed tomography (sCT) opens up new possibilities for the quantitative measurement of materials in an object compared with conventional energy-integrating CT or dual-energy CT. However, achieving reliable and accurate material decomposition in sCT is extremely challenging, due to the 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 spatioenergy segmentation of regions of interest (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 with common decomposition methods based on total variation (TV) or L1-norm (lasso) regularization. [ABSTRACT FROM AUTHOR]