Paper Info
Title
Sparse-View X-Ray Spectral Ct Reconstruction Using Annihilating Filter-Based Low Rank Hankel Matrix Approach
Abstract
In a kVp switching-based sparse view spectral CT, each spectral image cannot be reconstructed separably by an analytic reconstruction method, because the projection views for each spectral band is too sparse. However, the underlying structure is common between the spectral bands, so there exists inter-spectral redundancies that can be exploited by the recently proposed annihilating filter-based low rank Hankel matrix approach (ALOHA). More specifically, the sparse view projection data are first rebinned in the Fourier space, from which Hankel structured matrix with missing elements are constructed for each spectral band. Thanks to the inter-spectral correlations as well as transform domain sparsity of underlying images, the concatenated Hankel structured matrix is low-ranked, and the missing Fourier data for each spectral band can be simultaneously estimated using a low rank matrix completion. To reduce the computational complexity furthermore, we exploit the Hermitian symmetry of Fourier data. Numerical experiments confirm that the proposed method outperforms the existing ones.
Year
DOI
Venue
2016
10.1109/ISBI.2016.7493333
2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
Keywords
Field
DocType
spectral computed tomography (CT), sparse-view, X-ray CT, annihilating filter, low rank Hankel matrix
Frequency domain,Pattern recognition,Computer science,Matrix (mathematics),Fourier transform,Low-rank approximation,Hermitian function,Artificial intelligence,Spectral bands,Hankel matrix,Computational complexity theory
Conference
ISSN
Citations 
PageRank 
1945-7928
0
0.34
References 
Authors
4
4
Name
Order
Citations
PageRank
Yoseob Han11365.91
Kyong Hwan Jin221814.45
Kyung Sang Kim3226.56
Jong Chul Ye471579.99