Non-local Low-rank Cube-based Tensor Factorization for Spectral CT Reconstruction

by   Weiwen Wu, et al.

Spectral computed tomography (CT) reconstructs material-dependent attenuation images with the projections of multiple narrow energy windows, it is meaningful for material identification and decomposition. Unfortunately, the multi-energy projection dataset always contains strong complicated noise and result in the projections has a lower signal-noise-ratio (SNR). Very recently, the spatial-spectral cube matching frame (SSCMF) was proposed to explore the non-local spatial-spectrum similarities for spectral CT. The method constructs such a group by clustering up a series of non-local spatial-spectrum cubes. The small size of spatial patch for such a group make SSCMF fails to encode the sparsity and low-rank properties. In addition, the hard-thresholding and collaboration filtering operation in the SSCMF are also rough to recover the image features and spatial edges. While for all steps are operated on 4-D group, we may not afford such huge computational and memory load in practical. To avoid the above limitation and further improve image quality, we first formulate a non-local cube-based tensor instead of the group to encode the sparsity and low-rank properties. Then, as a new regularizer, Kronecker-Basis-Representation (KBR) tensor factorization is employed into a basic spectral CT reconstruction model to enhance the ability of extracting image features and protecting spatial edges, generating the non-local low-rank cube-based tensor factorization (NLCTF) method. Finally, the split-Bregman strategy is adopted to solve the NLCTF model. Both numerical simulations and realistic preclinical mouse studies are performed to validate and assess the NLCTF algorithm. The results show that the NLCTF method outperforms the other competitors.


page 1

page 6

page 7

page 8

page 9

page 10


Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography

Spectral computed tomography (CT) can reconstruct spectral images from d...

Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration

Non-local low-rank tensor approximation has been developed as a state-of...

Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection

Many state-of-the-art methods have been proposed for infrared small targ...

Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising

Non-local low-rank tensor approximation has been developed as a state-of...

An algorithm for improving Non-Local Means operators via low-rank approximation

We present a method for improving a Non Local Means operator by computin...

DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT

The potential huge advantage of spectral computed tomography (CT) is its...

Automated Selection of Uniform Regions for CT Image Quality Detection

CT images are widely used in pathology detection and follow-up treatment...