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ROI-Wise Material Decomposition in Spectral Photon-Counting CT

Abstract : Spectral photon-counting X-ray CT (sCT) opens up new possibilities for the quantitative measurement of materials in an object, compared to conventional energyintegrating 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 L1norm (lasso) regularization.
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Contributor : Yuemin Zhu Connect in order to contact the contributor
Submitted on : Thursday, November 18, 2021 - 6:51:12 PM
Last modification on : Friday, November 26, 2021 - 3:28:59 AM


XieBingqing_Ieee Tns-2020.pdf
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Bingqing Xie, Pei Niu, Ting Su, Valerie Kaftandjian, Loic Boussel, et al.. ROI-Wise Material Decomposition in Spectral Photon-Counting CT. IEEE Transactions on Nuclear Science, Institute of Electrical and Electronics Engineers, 2020, 67 (6), pp.1066-1075. ⟨10.1109/TNS.2020.2985071⟩. ⟨hal-03435696⟩



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