Photon-counting spectral CT reconstruction with sparse and double low-rank components fusion.

• Developing double low-rank constraint, adapting to the characteristic of spectral imaging. • Fusing complementary components, sparse component and double low-rank component, to obtain the final spectral tensor. • Obtaining Satisfying channel images and higher material decomposition accuracy. • Which shows the potential for diagnostically differential discrimination. Photon-counting CT (computed tomography) has aroused more attention. The relatively high dose of X-ray increases concerns about radiation, dose reduction can mitigate radiation risk, however, projection data within energy channel suffers from strong noise, leading to degraded results. Besides, there is high correlation among channels since the multi-channel data is captured from the same object, which should be utilized to improve reconstruction quality. To suppress noise while preserving details in reconstruction and improve material decomposition accuracy, we proposed a novel optimization-based spectral CT reconstruction method with a fusion framework. Two results were obtained by implementing two iterative reconstructions with different constraints and can be treated as complementary components. The first constraint is sparsity, the second constraint is double low-rank, moreover, a heuristic strategy is designed to solve double low-rank constrained optimization. We developed a guided filter based fusion framework to fuse sparse component and double low-rank component. Experimental results showed that our method achieved satisfying visualization, higher values of PSNR (peak signal-to-noise ratio) and SSIM (structure similarity), moreover, lower material decomposition error in this work. Conclusion. Our method can well perform spectral CT reconstruction under low-signal-to-ratio situation and it has decent performance in noise suppression, edge preserving and material discrimination. [ABSTRACT FROM AUTHOR]