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Photon-counting detector based spectral computed tomography (CT) has great potential in material decomposition, tissue characterization, lesion detection, and other applications. For a fixed total photon number or radiation dose, the increase of channel number will lead to the decrease of the photon number in each channel, resulting in degraded image quality of the reconstructed image. This is difficult to meet the practical applications for material decomposition, tissue characterization, lesion detection, etc. To improve the quality of image reconstruction, we propose a spectral CT reconstruction algorithm based on joint multi-channel total generalized variation (TGV) minimization and tensor decomposition. On one hand, the algorithm takes joint multi-channel TGV function as regularization. The total generalized variation is extended to the vector, and the sparsity of singular value is used to promote the linear dependence of the image gradient. On the other hand, the multi-channel images share the same physical structure, and the algorithm employs the non-local feature similarity in the image domain. Similar image blocks are clustered into a four-order tensor group, and the noise was reduced by sparse representation of high-dimensional tensors. Experiment results show the proposed algorithm can further improve the quality of reconstructed image and preserve the edge and details of the spectral CT image.
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