Paper Info
Title
FONT-SIR: Fourth-Order Nonlocal Tensor Decomposition Model for Spectral CT Image Reconstruction
Abstract
Spectral computed tomography (CT) reconstructs images from different spectral data through photon counting detectors (PCDs). However, due to the limited number of photons and the counting rate in the corresponding spectral segment, the reconstructed spectral images are usually affected by severe noise. In this paper, we propose a fourth-order nonlocal tensor decomposition model for spectral CT image reconstruction (FONT-SIR). To maintain the original spatial relationships among similar patches and improve the imaging quality, similar patches without vectorization are grouped in both spectral and spatial domains simultaneously to form the fourth-order processing tensor unit. The similarity of different patches is measured with the cosine similarity of latent features extracted using principal component analysis (PCA). By imposing the constraints of the weighted nuclear and total variation (TV) norms, each fourth-order tensor unit is decomposed into a low-rank component and a sparse component, which can efficiently remove noise and artifacts while preserving the structural details. Moreover, the alternating direction method of multipliers (ADMM) is employed to solve the decomposition model. Extensive experimental results on both simulated and real data sets demonstrate that the proposed FONT-SIR achieves superior qualitative and quantitative performance compared with several state-of-the-art methods.
Year
DOI
Venue
2022
10.1109/TMI.2022.3156270
IEEE Transactions on Medical Imaging
Keywords
DocType
Volume
Spectral CT,iterative reconstruction,tensor decomposition,nuclear norm
Journal
41
Issue
ISSN
Citations 
8
0278-0062
0
PageRank 
References 
Authors
0.34
34
8
Name
Order
Citations
PageRank
Chen Xiang13135.72
Wenjun Xia2104.83
Yan Liu392.88
Hu Chen4133.75
Jiliu Zhou545058.21
Zhiyuan Zha600.34
Bihan Wen722518.64
Zhang Yi835637.14