Iterative CT reconstruction based on ADMM using shearlet sparse regularization-Reference-Cited by-同舟云学术

Iterative CT reconstruction based on ADMM using shearlet sparse regularization

Published:2022 Issue:12 Volume:19 Page:11840-11853
ISSN:1551-0018
Container-title:Mathematical Biosciences and Engineering
language:
Short-container-title:MBE

Author:

Xiao Dayu¹,Li Jianhua¹,Zhao Ruotong²,Qi Shouliang¹,Kang Yan³

Affiliation:

1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110819, China

2. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China

3. College of Health Science and Environment Engineering, Shenzhen Technology University, Shenzhen 518118, China

Abstract

<abstract> The total variation (TV) method favors solutions with the piece-wise constant assumption of the desired image from sparse-view sampling, for example, simple geometric images with flat intensity. When the phantoms become more complex and contain complicated textures, for example, high-resolution phantom and lung CT images, the images reconstructed by TV regularization may lose their contrast and fine structures. One of the optimally sparse transforms for images, the shearlet transform, has C2 without discontinuities on C2 curves, giving excellent sensitive directional information as compared with other wavelet transform approaches. Here, we developed a Shearlet-Sparse Regularization (SSR) algorithm solved with the Alternating Direction Method of Multipliers (ADMM) to overcome this limitation. With the strengthened characteristics of SSR, we performed one simulation experiment and two real experiments using a NeuViz 64 X-ray CT scanning system to measure the performance and properties of proposed algorithm. The results demonstrate that the SSR method exhibits the advantage of providing high-quality directional information and contrast as compared with TV. </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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