Super-Energy-Resolution Material Decomposition for Spectral Photon-Counting CT Using Pixel-Wise Learning.

Xie B, Zhu Y, Niu P, Su T, Yang F, Wang L, Rodesch PA, Boussel L, Douek P, Duvauchelle P

IEEE Access, vol. 9, pp. 168485-168495, 2021, doi: 10.1109/ACCESS.2021.3134636.

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Abstract:

Spectral photon-counting CT offers novel potentialities to achieve quantitative decomposition of material components, in comparison with traditional energy-integrating CT or dual-energy CT. Nonetheless, achieving accurate material decomposition, especially for low-concentration materials, is still extremely challenging for current sCT, due to restricted energy resolution stemming from the trade-off between the number of energy bins and undesired factors such as quantum noise. We propose to improve material decomposition by introducing the notion of super-energy-resolution in sCT. The super-energy-resolution material decomposition consists in learning the relationship between simulation and physical phantoms in image domain. To this end, a coupled dictionary learning method is utilized to learn such relationship in a pixel-wise way. The results on both physical phantoms and in vivo data showed that for the same decomposition method using lasso regularization, the proposed super-energy-resolution method achieves much higher decomposition accuracy and detection ability in contrast to traditional image-domain decomposition method using L1-norm regularization.

Xie B, Zhu Y, Niu P, Su T, Yang F, Wang L, Rodesch PA, Boussel L, Douek P, Duvauchelle P. Super-Energy-Resolution Material Decomposition for Spectral Photon-Counting CT Using Pixel-Wise Learning. 
IEEE Access, vol. 9, pp. 168485-168495, 2021, doi: 10.1109/ACCESS.2021.3134636.

URL: https://ieeexplore.ieee.org/document/9645431