Multi-energy computed tomography reconstruction using an average image induced low-rank tensor decomposition with spatial-spectral total variation regularization

With an advanced photon counting detector, multi-energy computed tomography (MECT) can classify the photons according to the presetting thresholds and then acquire CT measurements from multiple energy bins. However, the number of the photons at one energy bin is limited compared with that in the conventional polychromatic spectrum. Therefore, the MECT images could suffer from noise-induced artifacts. To address this issue, in this work, we present a MECT reconstruction scheme which incorporates a low-rank tensor decomposition with spatial-spectral total variation (LRTD-SSTV) regularization. Additionally, the prior information from the whole energy, i.e., the average image from the MECT images, is introduced to the LRTDSSTV regularization to further improve reconstruction performance. This reconstruction scheme is termed as "LRTD-SSTVavi". Experimental results with a digital phantom demonstrate that the presented method produces better MECT images and more accurate basis images compared with the RPCA, TDL and LRTD-STTV methods.