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Volume 42, Issue 4
A Nonlocal Kronecker-Basis-Representation Method for Low-Dose CT Sinogram Recovery

Jian Lu, Huaxuan Hu, Yuru Zou, Zhaosong Lu, Xiaoxia Liu, Keke Zu & Lin Li

DOI: 10.4208/jcm.2301-m2022-0091

J. Comp. Math., 42 (2024), pp. 1080-1108.

Published online: 2024-04

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  • Abstract

Low-dose computed tomography (LDCT) contains the mixed noise of Poisson and Gaussian, which makes the image reconstruction a challenging task. In order to describe the statistical characteristics of the mixed noise, we adopt the sinogram preprocessing as a standard maximum a posteriori (MAP). Based on the fact that the sinogram of LDCT has nonlocal self-similarity property, it exhibits low-rank characteristics. The conventional way of solving the low-rank problem is implemented in matrix forms, and ignores the correlations among similar patch groups. To avoid this issue, we make use of a nonlocal Kronecker-Basis-Representation (KBR) method to depict the low-rank problem. A new denoising model, which consists of the sinogram preprocessing for data fidelity and the nonlocal KBR term, is developed in this work. The proposed denoising model can better illustrate the generative mechanism of the mixed noise and the prior knowledge of the LDCT. Numerical results show that the proposed denoising model outperforms the state-of-the-art algorithms in terms of peak-signal-to-noise ratio (PSNR), feature similarity (FSIM), and normalized mean square error (NMSE).

  • Keywords

Low-dose computed tomography, Kronecker-basis-representation, Low-rank approximation, Noise-generating-mechanism.

  • AMS Subject Headings

92C55, 68U10, 65K05

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{JCM-42-1080, author = {Lu , JianHu , HuaxuanZou , YuruLu , ZhaosongLiu , XiaoxiaZu , Keke and Li , Lin}, title = {A Nonlocal Kronecker-Basis-Representation Method for Low-Dose CT Sinogram Recovery}, journal = {Journal of Computational Mathematics}, year = {2024}, volume = {42}, number = {4}, pages = {1080--1108}, abstract = {

Low-dose computed tomography (LDCT) contains the mixed noise of Poisson and Gaussian, which makes the image reconstruction a challenging task. In order to describe the statistical characteristics of the mixed noise, we adopt the sinogram preprocessing as a standard maximum a posteriori (MAP). Based on the fact that the sinogram of LDCT has nonlocal self-similarity property, it exhibits low-rank characteristics. The conventional way of solving the low-rank problem is implemented in matrix forms, and ignores the correlations among similar patch groups. To avoid this issue, we make use of a nonlocal Kronecker-Basis-Representation (KBR) method to depict the low-rank problem. A new denoising model, which consists of the sinogram preprocessing for data fidelity and the nonlocal KBR term, is developed in this work. The proposed denoising model can better illustrate the generative mechanism of the mixed noise and the prior knowledge of the LDCT. Numerical results show that the proposed denoising model outperforms the state-of-the-art algorithms in terms of peak-signal-to-noise ratio (PSNR), feature similarity (FSIM), and normalized mean square error (NMSE).

}, issn = {1991-7139}, doi = {https://doi.org/10.4208/jcm.2301-m2022-0091}, url = {http://global-sci.org/intro/article_detail/jcm/23047.html} }
TY - JOUR T1 - A Nonlocal Kronecker-Basis-Representation Method for Low-Dose CT Sinogram Recovery AU - Lu , Jian AU - Hu , Huaxuan AU - Zou , Yuru AU - Lu , Zhaosong AU - Liu , Xiaoxia AU - Zu , Keke AU - Li , Lin JO - Journal of Computational Mathematics VL - 4 SP - 1080 EP - 1108 PY - 2024 DA - 2024/04 SN - 42 DO - http://doi.org/10.4208/jcm.2301-m2022-0091 UR - https://global-sci.org/intro/article_detail/jcm/23047.html KW - Low-dose computed tomography, Kronecker-basis-representation, Low-rank approximation, Noise-generating-mechanism. AB -

Low-dose computed tomography (LDCT) contains the mixed noise of Poisson and Gaussian, which makes the image reconstruction a challenging task. In order to describe the statistical characteristics of the mixed noise, we adopt the sinogram preprocessing as a standard maximum a posteriori (MAP). Based on the fact that the sinogram of LDCT has nonlocal self-similarity property, it exhibits low-rank characteristics. The conventional way of solving the low-rank problem is implemented in matrix forms, and ignores the correlations among similar patch groups. To avoid this issue, we make use of a nonlocal Kronecker-Basis-Representation (KBR) method to depict the low-rank problem. A new denoising model, which consists of the sinogram preprocessing for data fidelity and the nonlocal KBR term, is developed in this work. The proposed denoising model can better illustrate the generative mechanism of the mixed noise and the prior knowledge of the LDCT. Numerical results show that the proposed denoising model outperforms the state-of-the-art algorithms in terms of peak-signal-to-noise ratio (PSNR), feature similarity (FSIM), and normalized mean square error (NMSE).

Lu , JianHu , HuaxuanZou , YuruLu , ZhaosongLiu , XiaoxiaZu , Keke and Li , Lin. (2024). A Nonlocal Kronecker-Basis-Representation Method for Low-Dose CT Sinogram Recovery. Journal of Computational Mathematics. 42 (4). 1080-1108. doi:10.4208/jcm.2301-m2022-0091
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